Seasonality of Plasmodium falciparum transmission: a systematic review

Malaria in areas under mining activity in the Amazon: A review

Deforestation and high human mobility due to mining activities have been key to the increase in malaria cases in the Americas. Here, we review the epidemiological and control aspects of malaria in the Amazon mining areas. Epidemiological evidence shows: 1) a positive correlation between illegal mining activity and malaria incidence, mostly in the Amazon region; 2) most Brazilian miners are males aged 15-29 years who move between states and even countries; 3) miners do not fear the disease and rely on medical care, diagnosis, and medication when they become ill; 4) illegal mining has emerged as the most reported anthropogenic activity within indigenous lands and is identified as a major cause of malaria outbreaks among indigenous people in the Amazon; and 5) because mining is largely illegal, most areas are not covered by any healthcare facilities or activities, leading to little assistance in the diagnosis and treatment of malaria. Our review identified five strategies for reducing the malaria incidence in areas with mining activities: 1) reviewing legislation to control deforestation and mining expansion, particularly in indigenous lands; 2) strengthening malaria surveillance by expanding the network of community health agents to support rapid diagnosis and treatment; 3) reinforcing vector control strategies, such as the use of insecticide-treated nets; 4) integrating deforestation alerts into the national malaria control program; and 5) implementing multi-sectoral activities and providing prompt assistance to indigenous populations. With this roadmap, we can expect a decrease in malaria incidence in the Amazonian mining areas in the future.

Malaria among members of the U.S. Armed Forces, 2023

MSMR publishes annual updates on the incidence of malaria among U.S. service members. Malaria infection remains a potential health threat to U.S. service members located in or near endemic areas due to duty assignment, participation in contingency operations, or personal travel. In 2023, a total of 39 active and reserve component service members were diagnosed with or reported to have malaria, an 8.3% increase from the 36 cases identified in 2022. Over half of the malaria cases in 2023 were caused by Plasmodium falciparum (53.8%; n=21) followed by unspecified types of malaria (35.9%; n=14) and P vivax and other Plasmodia (5.1%; n=2 each ). Malaria cases were diagnosed or reported from 22 different medical facilities: 18 in the U.S., 2 in Germany, 1 in Africa, 1 in South Korea. Of the 33 cases with known locations of diagnoses, 6 (18.2%) were reported from or diagnosed outside the U.S.

Exploring malaria prediction models in Togo: a time series forecasting by health district and target group

OBJECTIVES

Integrating malaria prediction models into malaria control strategies can help to anticipate the response to seasonal epidemics. This study aimed to explore the possibility of using routine malaria data and satellite-derived climate data to forecast malaria cases in Togo.

METHODS

Generalised additive (mixed) models were developed to forecast the monthly number of malaria cases in 40 health districts and three target groups. Routinely collected malaria data from 2013 to 2016 and meteorological and vegetation data with a time lag of 1 or 2 months were used for model training, while the year 2017 was used for model testing. Two methods for selecting lagged meteorological and environmental variables were compared: a first method based on statistical approach ('SA') and a second method based on biological reasoning ('BR'). Both methods were applied to obtain a model per target group and health district and a mixed model per target group and health region with the health district as a random effect. The predictive skills of the four models were compared for each health district and target group.

RESULTS

The most selected predictors in the models per district for the 'SA' method were the normalised difference vegetation index, minimum temperature and mean temperature. The 'SA' method provided the most accurate models for the training period, except for some health districts in children ≥5 years old and adults and in pregnant women. The most accurate models for the testing period varied by health district and target group, provided either by the 'SA' method or the 'BR' method. Despite the development of models with four different approaches, the number of malaria cases was inaccurately forecasted.

CONCLUSIONS

These models cannot be used as such in malaria control activities in Togo. The use of finer spatial and temporal scales and non-environmental data could improve malaria prediction.

Effects of Climate Variability on Malaria Transmission in Southern Côte d'Ivoire, West Africa

Malaria continues to be a major public health concern with a substantial burden in Africa. Even though it has been widely demonstrated that malaria transmission is climate-driven, there have been very few studies assessing the relationship between climate variables and malaria transmission in Côte d'Ivoire. We used the VECTRI model to predict malaria transmission in southern Côte d'Ivoire. First, we tested the suitability of VECTRI in modeling malaria transmission using ERA5 temperature data and ARC2 rainfall data. We then used the projected climatic data pertaining to 2030, 2050, and 2080 from a set of 14 simulations from the CORDEX-Africa database to compute VECTRI outputs. The entomological inoculation rate (EIR) from the VECTRI model was well correlated with the observed malaria cases from 2010 to 2019, including the peaks of malaria cases and the EIR. However, the correlation between the two parameters was not statistically significant. The VECTRI model predicted an increase in malaria transmissions in both scenarios (RCP8.5 and RCP4.5) for the time period 2030 to 2080. The monthly EIR for RCP8.5 was very high (1.74 to 1131.71 bites/person) compared to RCP4.5 (0.48 to 908 bites/person). These findings call for greater efforts to control malaria that take into account the impact of climatic factors.

Climate Drivers of Malaria Transmission Seasonality and Their Relative Importance in Sub-Saharan Africa

A new database of the Entomological Inoculation Rate (EIR) was used to directly link the risk of infectious mosquito bites to climate in Sub-Saharan Africa. Applying a statistical mixed model framework to high-quality monthly EIR measurements collected from field campaigns in Sub-Saharan Africa, we analyzed the impact of rainfall and temperature seasonality on EIR seasonality and determined important climate drivers of malaria seasonality across varied climate settings in the region. We observed that seasonal malaria transmission was within a temperature window of 15°C-40°C and was sustained if average temperature was well above 15°C or below 40°C. Monthly maximum rainfall for seasonal malaria transmission did not exceed 600 in west Central Africa, and 400 mm in the Sahel, Guinea Savannah, and East Africa. Based on a multi-regression model approach, rainfall and temperature seasonality were found to be significantly associated with malaria seasonality in all parts of Sub-Saharan Africa except in west Central Africa. Topography was found to have significant influence on which climate variable is an important determinant of malaria seasonality in East Africa. Seasonal malaria transmission onset lags behind rainfall only at markedly seasonal rainfall areas such as Sahel and East Africa; elsewhere, malaria transmission is year-round. High-quality EIR measurements can usefully supplement established metrics for seasonal malaria. The study's outcome is important for the improvement and validation of weather-driven dynamical mathematical malaria models that directly simulate EIR. Our results can contribute to the development of fit-for-purpose weather-driven malaria models to support health decision-making in the fight to control or eliminate malaria in Sub-Saharan Africa.

Global malaria infection risk from climate change

As a long-standing public health issue, malaria still severely affects many parts of the world, especially Africa. With greenhouse gas emissions, temperatures continue to rise. Based on diverse shared socioeconomic pathways (SSPs), future temperatures can be estimated. However, the impacts of climate change on malaria infection rates in all epidemic regions are unknown. Here, we estimate the differences in global malaria infection rates predicted under different SSPs during several periods as well as malaria infection case changes (MICCs) resulting from those differences. Our results indicate that the global MICCs resulting from the conversion from SSP1-2.6 to SSP2-4.5, to SSP3-7.0, and to SSP5-8.5 are 6.506 (with a 95% uncertainty interval [UI] of 6.150-6.861) million, 3.655 (3.416-3.894) million, and 2.823 (2.635-3.012) million, respectively, from 2021 to 2040; these values represent increases of 2.699%, 1.517%, and 1.171%, respectively, compared to the 241 million infection cases reported in 2020. Temperatures increases will adversely affect malaria the most in Africa during the 2021-2040 period. From 2081 to 2100, the MICCs obtained for the three scenario shifts listed above are -79.109 (-83.626 to -74.591) million, -238.337 (-251.920 to -0.141) million, and -162.692 (-174.628 to -150.757) million, corresponding to increases of -32.825%, -98.895%, and -67.507%, respectively. Climate change will increase the danger and risks associated with malaria in the most vulnerable regions in the near term, thus aggravating the difficulty of eliminating malaria. Reducing GHG emissions is a potential pathway to protecting people from malaria.

Influence of landscape heterogeneity on entomological and parasitological indices of malaria in Kisumu, Western Kenya

Background

Identification and characterization of larval habitats, documentation of Anopheles spp. composition and abundance, and Plasmodium spp. infection burden are critical components of integrated vector management. The present study aimed to investigate the effect of landscape heterogeneity on entomological and parasitological indices of malaria in western Kenya.

Methods

A cross-sectional entomological and parasitological survey was conducted along an altitudinal transect in three eco-epidemiological zones: lakeshore along the lakeside, hillside, and highland plateau during the wet and dry seasons in 2020 in Kisumu County, Kenya. Larval habitats for Anopheles mosquitoes were identified and characterized. Adult mosquitoes were sampled using pyrethrum spray catches (PSC). Finger prick blood samples were taken from residents and examined for malaria parasites by real-time PCR (RT-PCR).

Results

Increased risk of Plasmodium falciparum infection was associated with residency in the lakeshore zone, school-age children, rainy season, and no ITNs (χ² = 41.201, df = 9, P < 0.0001). Similarly, lakeshore zone and the rainy season significantly increased Anopheles spp. abundance. However, house structures such as wall type and whether the eave spaces were closed or open, as well as the use of ITNs, did not affect Anopheles spp. densities in the homes (χ² = 38.695, df = 7, P < 0.0001). Anopheles funestus (41.8%) and An. arabiensis (29.1%) were the most abundant vectors in all zones. Sporozoite prevalence was 5.6% and 3.2% in the two species respectively. The lakeshore zone had the highest sporozoite prevalence (4.4%, 7/160) and inoculation rates (135.2 infective bites/person/year). High larval densities were significantly associated with lakeshore zone and hillside zones, animal hoof prints and tire truck larval habitats, wetland and pasture land, and the wet season. The larval habitat types differed significantly across the landscape zones and seasonality (χ² = 1453.044, df = 298, P < 0.0001).

Conclusion

The empirical evidence on the impact of landscape heterogeneity and seasonality on vector densities, parasite transmission, and Plasmodium infections in humans emphasizes the importance of tailoring specific adaptive environmental management interventions to specific landscape attributes to have a significant impact on transmission reduction.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05447-9.

The relative impact of interventions on sympatric Plasmodium vivax and Plasmodium falciparum malaria: A systematic review

BACKGROUND

In areas with both Plasmodium vivax and Plasmodium falciparum malaria, interventions can reduce the burden of both species but the impact may vary due to their different biology. Knowing the expected relative impact on the two species over time for vector- and drug-based interventions, and the factors affecting this, could help plan and evaluate intervention strategies.

METHODS

For three interventions (treated bed nets (ITN), mass drug administration (MDA) and indoor residual spraying (IRS)), we identified studies providing information on the proportion of clinical illness and patent infections attributed to P. vivax over time using a literature search. The change in the proportion of malaria attributed to P. vivax up to two years since implementation was estimated using logistic regression accounting for clustering with random effects. Potential factors (intervention type, coverage, relapse pattern, transmission intensity, seasonality, initial proportion of P. vivax and round of intervention) were assessed.

RESULTS

In total there were 55 studies found that led to 72 series of time-points for clinical case data and 69 series for patent infection data. The main reason of study exclusion was insufficient information on interventions. There was considerable variation in the proportion of malaria attributed to P. vivax over time by study and location for all of the interventions. Overall, there was an increase apart from MDA in the short-term. The potential factors could not be ruled in or out. Although not consistently significant, coverage, transmission intensity and relapse pattern are possible factors that explain some of the variation found.

CONCLUSION

While there are reports of an increase in the proportion of malaria due to P. vivax following interventions in the long-term, there was substantial variation for the shorter time-scales considered in this study (up to 24 months for IRS and ITN, and up to six months for MDA). The large variability points to the need for the monitoring of both species after an intervention. Studies should report intervention timing and characteristics to allow inclusion in systematic reviews.

Risk associations of submicroscopic malaria infection in lakeshore, plateau and highland areas of Kisumu County in western Kenya

BACKGROUND

Persons with submicroscopic malaria infection are a major reservoir of gametocytes that sustain malaria transmission in sub-Saharan Africa. Despite recent decreases in the national malaria burden in Kenya due to vector control interventions, malaria transmission continues to be high in western regions of the country bordering Lake Victoria. The objective of this study was to advance knowledge of the topographical, demographic and behavioral risk factors associated with submicroscopic malaria infection in the Lake Victoria basin in Kisumu County.

METHODS

Cross-sectional community surveys for malaria infection were undertaken in three eco-epidemiologically distinct zones in Nyakach sub-County, Kisumu. Adjacent regions were topologically characterized as lakeshore, hillside and highland plateau. Surveys were conducted during the 2019 and 2020 wet and dry seasons. Finger prick blood smears and dry blood spots (DBS) on filter paper were collected from 1,777 healthy volunteers for microscopic inspection and real time-PCR (RT-PCR) diagnosis of Plasmodium infection. Persons who were PCR positive but blood smear negative were considered to harbor submicroscopic infections. Topographical, demographic and behavioral risk factors were correlated with community prevalence of submicroscopic infections.

RESULTS

Out of a total of 1,777 blood samples collected, 14.2% (253/1,777) were diagnosed as submicroscopic infections. Blood smear microscopy and RT-PCR, respectively, detected 3.7% (66/1,777) and 18% (319/1,777) infections. Blood smears results were exclusively positive for P. falciparum, whereas RT-PCR also detected P. malariae and P. ovale mono- and co-infections. Submicroscopic infection prevalence was associated with topographical variation (χ2 = 39.344, df = 2, p<0.0001). The highest prevalence was observed in the lakeshore zone (20.6%, n = 622) followed by the hillside (13.6%, n = 595) and highland plateau zones (7.9%, n = 560). Infection prevalence varied significantly according to season (χ2 = 17.374, df = 3, p<0.0001). The highest prevalence was observed in residents of the lakeshore zone in the 2019 dry season (29.9%, n = 167) and 2020 and 2019 rainy seasons (21.5%, n = 144 and 18.1%, n = 155, respectively). In both the rainy and dry seasons the likelihood of submicroscopic infection was higher in the lakeshore (AOR: 2.71, 95% CI = 1.85-3.95; p<0.0001) and hillside (AOR: 1.74, 95% CI = 1.17-2.61, p = 0.007) than in the highland plateau zones. Residence in the lakeshore zone (p<0.0001), male sex (p = 0.025), school age (p = 0.002), and living in mud houses (p = 0.044) increased the risk of submicroscopic malaria infection. Bed net use (p = 0.112) and occupation (p = 0.116) were not associated with submicroscopic infection prevalence.

CONCLUSION

Topographic features of the local landscape and seasonality are major correlates of submicroscopic malaria infection in the Lake Victoria area of western Kenya. Diagnostic tests more sensitive than blood smear microscopy will allow for monitoring and targeting geographic sites where additional vector interventions are needed to reduce malaria transmission.

Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study

OBJECTIVES

Malaria is a vector-borne disease that remains a serious public health problem due to its climatic sensitivity. Accurate prediction of malaria re-emergence is very important in taking corresponding effective measures. This study aims to investigate the impact of climatic factors on the re-emergence of malaria in mainland China.

DESIGN

A modelling study.

SETTING AND PARTICIPANTS

Monthly malaria cases for four Plasmodium species (P. falciparum, P. malariae, P. vivax and other Plasmodium) and monthly climate data were collected for 31 provinces; malaria cases from 2004 to 2016 were obtained from the Chinese centre for disease control and prevention and climate parameters from China meteorological data service centre. We conducted analyses at the aggregate level, and there was no involvement of confidential information.

PRIMARY AND SECONDARY OUTCOME MEASURES

The long short-term memory sequence-to-sequence (LSTMSeq2Seq) deep neural network model was used to predict the re-emergence of malaria cases from 2004 to 2016, based on the influence of climatic factors. We trained and tested the extreme gradient boosting (XGBoost), gated recurrent unit, LSTM, LSTMSeq2Seq models using monthly malaria cases and corresponding meteorological data in 31 provinces of China. Then we compared the predictive performance of models using root mean squared error (RMSE) and mean absolute error evaluation measures.

RESULTS

The proposed LSTMSeq2Seq model reduced the mean RMSE of the predictions by 19.05% to 33.93%, 18.4% to 33.59%, 17.6% to 26.67% and 13.28% to 21.34%, for P. falciparum, P. vivax, P. malariae, and other plasmodia, respectively, as compared with other candidate models. The LSTMSeq2Seq model achieved an average prediction accuracy of 87.3%.

CONCLUSIONS

The LSTMSeq2Seq model significantly improved the prediction of malaria re-emergence based on the influence of climatic factors. Therefore, the LSTMSeq2Seq model can be effectively applied in the malaria re-emergence prediction.

A decade of travel-associated malaria at King Fahad Hospital of the University in the Eastern Province of Saudi Arabia

Travel-associated malaria is a health hazard, even in non-malaria endemic regions. This is a hospital-based retrospective study of 12,931 febrile patients who presented at King Fahad Hospital of the University (KFHU) from January 2009 to December 2019. Patients either returning from malaria endemic countries and/or for whom malaria was suspected, had blood films microscopically screened for malaria parasites. Malaria prevalence was very low in febrile patients attending KFHU. Out of the 12,931 febrile patients, 0.63% (n = 81) were malaria positive, all travel-related, except for one case of transfusion malaria. Indian nationals were the most infected (29.6%, n = 24), followed by Sudanese nationals (24.7%, n = 20). P. falciparum (47%, n = 38) and P. vivax (42%, n = 24) were the predominant species. The majority of P. falciparum (64.5%, n = 20) cases were from African nationals and the majority of P. vivax (72.7%, n = 24) cases were from Asia. The highest percentage of malaria patients were adult (90%, n = 73), males (85.2%, n = 69), ages ranged from 6 to 65, with a mean of 34.6 years. Most of the malaria cases presented at the emergency room (ER), only 3 required critical care. Only sex, hospitalized in-patient (IP) and attendance at ER were statistically associated with malaria. In the presence of a potential vector, travel-associated malaria in non-malaria endemic areas should be monitored to guide control strategies.

Author summary: Malaria is a neglected potentially fatal tropical mosquito-born disease. Travel-associated malaria is a health hazard, even in non-malaria endemic regions. In spite of previous efforts to estimate malaria prevalence, morbidity and mortality in Saudi Arabia in the last decade, there have been no studies that determine the prevalence of malaria in Al-Khobar, Eastern Province of Saudi Arabia. Malaria prevalence was very low in febrile patients (81/12,931) attending King Fahad Hospital of the University over a decade. Cases were all travel-related, except for one case of transfusion malaria. Indian nationals were the most infected (29.6%), followed by Sudanese nationals (24.7%). P. falciparum (47%) and P. vivax (42%) were the predominant species. The majority of P. falciparum (64.5%) cases were from Africa and the majority of P. vivax (72.7%) cases were from Asia. No patient factors predicted malaria in febrile travelers. In non-malaria endemic areas, in the presence of a potential vector, patients with acute fever coming from endemic areas or having received blood transfusion, should be screened for travel-associated malaria to guide control strategies.

Seasonality of confirmed malaria cases from 2008 to 2017 in Togo: a time series analysis by health district and target group

Background

This study aimed to assess the seasonality of confirmed malaria cases in Togo and to provide new indicators of malaria seasonality to the National Malaria Control Programme (NMCP).

Methods

Aggregated data of confirmed malaria cases were collected monthly from 2008 to 2017 by the Togo’s NMCP and stratified by health district and according to three target groups: children < 5 years old, children ≥ 5 years old and adults, and pregnant women. Time series analysis was carried out for each target group and health district. Seasonal decomposition was used to assess the seasonality of confirmed malaria cases. Maximum and minimum seasonal indices, their corresponding months, and the ratio of maximum/minimum seasonal indices reflecting the importance of malaria transmission, were provided by health district and target group.

Results

From 2008 to 2017, 7,951,757 malaria cases were reported in Togo. Children < 5 years old, children ≥ 5 years old and adults, and pregnant women represented 37.1%, 57.7% and 5.2% of the confirmed malaria cases, respectively. The maximum seasonal indices were observed during or shortly after a rainy season and the minimum seasonal indices during the dry season between January and April in particular. In children < 5 years old, the ratio of maximum/minimum seasonal indices was higher in the north, suggesting a higher seasonal malaria transmission, than in the south of Togo. This is also observed in the other two groups but to a lesser extent.

Conclusions

This study contributes to a better understanding of malaria seasonality in Togo. The indicators of malaria seasonality could allow for more accurate forecasting in malaria interventions and supply planning throughout the year.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06893-z.

Forecasting Confirmed Malaria Cases in Northwestern Province of Zambia: A Time Series Analysis Using 2014–2020 Routine Data

Background. Malaria remains a significant public health problem, especially in resource-poor settings. We aimed to forecast the year 2021 monthly confirmed malaria cases in the northwestern province of Zambia. Methods. The total number of confirmed monthly malaria cases recorded at health facilities over the past 7-years period (January 2014 to December 2020) was taken from the District Health Information System version 2 (DHIS.2) database. Box–Jenkins autoregressive integrated moving average (ARIMA) was used to forecast monthly confirmed malaria cases for 2021. STATA software version 16 was used for analyzing the time series data. Results. Between 2014 and 2020, there were 3,795,541 confirmed malaria cases in the northwestern province with a monthly mean of 45,185 cases. ARIMA (2, 1, 2) (0, 1, 1)12 was the best fit and the most parsimonious model. The forecasted mean monthly confirmed malaria cases were 60,284 (95%CI 30,969–121,944), and the total forecasted confirmed malaria cases were 723,413 (95%CI 371,626–1,463,322) for the year 2021. Conclusion. The forecasted confirmed malaria cases suggest that there will be an increase in the number of confirmed malaria cases for the year 2021 in the northwestern province. Therefore, there is a need for concerted efforts to prevent and eliminate the disease if the goal to eliminate malaria in Zambia by 2030 is to be realized.

Seasonal patterns of malaria, genital infection, nutritional and iron status in non-pregnant and pregnant adolescents in Burkina Faso: a secondary analysis of trial data

Background

Adolescents are considered at high risk of developing iron deficiency. Studies in children indicate that the prevalence of iron deficiency increased with malaria transmission, suggesting malaria seasonally may drive iron deficiency. This paper examines monthly seasonal infection patterns of malaria, abnormal vaginal flora, chorioamnionitis, antibiotic and antimalarial prescriptions, in relation to changes in iron biomarkers and nutritional indices in adolescents living in a rural area of Burkina Faso, in order to assess the requirement for seasonal infection control and nutrition interventions.

Methods

Data collected between April 2011 and January 2014 were available for an observational seasonal analysis, comprising scheduled visits for 1949 non-pregnant adolescents (≤19 years), (315 of whom subsequently became pregnant), enrolled in a randomised trial of periconceptional iron supplementation. Data from trial arms were combined. Body Iron Stores (BIS) were calculated using an internal regression for ferritin to allow for inflammation. At recruitment 11% had low BIS (< 0 mg/kg). Continuous outcomes were fitted to a mixed-effects linear model with month, age and pregnancy status as fixed effect covariates and woman as a random effect. Dichotomous infection outcomes were fitted with analogous logistic regression models.

Results

Seasonal variation in malaria parasitaemia prevalence ranged between 18 and 70% in non-pregnant adolescents (P < 0.001), peaking at 81% in those who became pregnant. Seasonal variation occurred in antibiotic prescription rates (0.7–1.8 prescriptions/100 weekly visits, P < 0.001) and chorioamnionitis prevalence (range 15–68%, P = 0.026). Mucosal vaginal lactoferrin concentration was lower at the end of the wet season (range 2–22 μg/ml, P < 0.016), when chorioamnionitis was least frequent. BIS fluctuated annually by up to 53.2% per year around the mean BIS (5.1 mg/kg², range 4.1–6.8 mg/kg), with low BIS (< 0 mg/kg) of 8.7% in the dry and 9.8% in the wet seasons (P = 0.36). Median serum transferrin receptor increased during the wet season (P < 0.001). Higher hepcidin concentration in the wet season corresponded with rising malaria prevalence and use of prescriptions, but with no change in BIS. Mean Body Mass Index and Mid-Upper-Arm-Circumference values peaked mid-dry season (both P < 0.001).

Conclusions

Our analysis supports preventive treatment of malaria among adolescents 15–19 years to decrease their disease burden, especially asymptomatic malaria. As BIS were adequate in most adolescents despite seasonal malaria, a requirement for programmatic iron supplementation was not substantiated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11819-0.

The Problem of Microbial Dark Matter in Neonatal Sepsis

Neonatal sepsis (NS) kills 750,000 infants every year. Effectively treating NS requires timely diagnosis and antimicrobial therapy matched to the causative pathogens, but most blood cultures for suspected NS do not recover a causative pathogen. We refer to these suspected but unidentified pathogens as microbial dark matter. Given these low culture recovery rates, many non–culture-based technologies are being explored to diagnose NS, including PCR, 16S amplicon sequencing, and whole metagenomic sequencing. However, few of these newer technologies are scalable or sustainable globally. To reduce worldwide deaths from NS, one possibility may be performing population-wide pathogen discovery. Because pathogen transmission patterns can vary across space and time, computational models can be built to predict the pathogens responsible for NS by region and season. This approach could help to optimally treat patients, decreasing deaths from NS and increasing antimicrobial stewardship until effective diagnostics that are scalable become available globally.

A Bayesian Spatio-Temporal Analysis of Malaria in the Greater Accra Region of Ghana from 2015 to 2019

The Greater Accra Region is the smallest of the 16 administrative regions in Ghana. It is highly populated and characterized by tropical climatic conditions. Although efforts towards malaria control in Ghana have had positive impacts, malaria remains in the top five diseases reported at healthcare facilities within the Greater Accra Region. To further accelerate progress, analysis of regionally generated data is needed to inform control and management measures at this level. This study aimed to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation in malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using seasonal-trend decomposition, based on locally weighted regression to analyze seasonality. A negative binomial regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases were recorded in the region from 2015 to 2019. The overall malaria incidence for the region was approximately 47 per 1000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Monthly malaria case incidence was found to decrease by 2.3% (95% credible interval: 0.7–4.2%) for each 1 °C increase in monthly minimum temperature. Only five districts located in the south-central part of the region had a malaria incidence rate lower than the regional average at >95% probability level. The distribution of malaria cases was heterogeneous, seasonal, and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.

Host specificity of Hepatocystis infection in short-nosed fruit bats (Cynopterus brachyotis) in Singapore

Haemosporidians infect a wide diversity of bat genera and species, yet little is known about their transmission cycles or epidemiology. Though several recent studies have focused on the genus Hepatocystis, an Old World parasite primarily infecting bats, monkeys, and squirrels, this group is still understudied with little known about its transmission and molecular ecology. These parasites lack an asexual erythrocytic stage, making them unique from the Plasmodium vertebrate life cycle. In this study, we detected a prevalence of 31% of Hepatocystis in short-nosed fruit bats (Cynopterus brachyotis) in Singapore. Phylogenetic reconstruction with a partial cytochrome b sequence revealed a monophyletic group of Hepatocystis from C. brachyotis in Malaysia, Singapore, and Thailand. There was no relationship with infection and bat age, sex, location, body condition or monsoon season. The absence of this parasite in the five other bat species sampled in Singapore indicates this Hepatocystis species may be host restricted.

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A bat haemosporidian (Hepatocystis) was detected in short nose fruit bats (Cynopterus brachyotis) in Singapore.

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Infection was not associated with bat age, sex, sample location, body condition or monsoon season.

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Infection was detected in only one bat species, indicating this Hepatocystis species may be host specific.

Estimating the extrinsic incubation period of malaria using a mechanistic model of sporogony

During sporogony, malaria-causing parasites infect a mosquito, reproduce and migrate to the mosquito salivary glands where they can be transmitted the next time blood feeding occurs. The time required for sporogony, known as the extrinsic incubation period (EIP), is an important determinant of malaria transmission intensity. The EIP is typically estimated as the time for a given percentile, x, of infected mosquitoes to develop salivary gland sporozoites (the infectious parasite life stage), which is denoted by EIPx. Many mechanisms, however, affect the observed sporozoite prevalence including the human-to-mosquito transmission probability and possibly differences in mosquito mortality according to infection status. To account for these various mechanisms, we present a mechanistic mathematical model, which explicitly models key processes at the parasite, mosquito and observational scales. Fitting this model to experimental data, we find greater variation in the EIP than previously thought: we estimated the range between EIP10 and EIP90 (at 27°C) as 4.5 days compared to 0.9 days using existing statistical methods. This pattern holds over the range of study temperatures included in the dataset. Increasing temperature from 21°C to 34°C decreased the EIP50 from 16.1 to 8.8 days. Our work highlights the importance of mechanistic modelling of sporogony to (1) improve estimates of malaria transmission under different environmental conditions or disease control programs and (2) evaluate novel interventions that target the mosquito life stages of the parasite.

Spatio-Temporal Dynamics of Plasmodium falciparum and Plasmodium vivax in French Guiana: 2005-2019

Aims: This study examines the dynamics of malaria as influenced by meteorological factors in French Guiana from 2005 to 2019. It explores spatial hotspots of malaria transmission and aims to determine the factors associated with variation of hotspots with time. Methods: Data for individual malaria cases came from the surveillance system of the Delocalized Centers for Prevention and Care (CDPS) (n = 17) from 2005–2019. Meteorological data was acquired from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) database. The Box–Jenkins autoregressive integrated moving average (ARIMA) model tested stationarity of the time series, and the impact of meteorological indices (issued from principal component analysis—PCA) on malaria incidence was determined with a general additive model. Hotspot characterization was performed using spatial scan statistics. Results: The current sample includes 7050 eligible Plasmodium vivax (n = 4111) and Plasmodium falciparum (n = 2939) cases from health centers across French Guiana. The first and second PCA-derived meteorological components (maximum/minimum temperature/minimum humidity and maximum humidity, respectively) were significantly negatively correlated with total malaria incidence with a lag of one week and 10 days, respectively. Overall malaria incidence decreased across the time series until 2017 when incidence began to trend upwards. Hotspot characterization revealed a few health centers that exhibited spatial stability across the entire time series: Saint Georges de l’Oyapock and Antecume Pata for P. falciparum, and Saint Georges de l’Oyapock, Antecume Pata, Régina and Camopi for P. vivax. Conclusions: This study highlighted changing malaria incidence in French Guiana and the influences of meteorological factors on transmission. Many health centers showed spatial stability in transmission, albeit not temporal. Knowledge of the areas of high transmission as well as how and why transmission has changed over time can inform strategies to reduce the transmission of malaria in French Guiana. Hotspots should be further investigated to understand other influences on local transmission, which will help to facilitate elimination.

The stationary distribution and stochastic persistence for a class of disease models: Case study of malaria

This paper presents a nonlinear family of stochastic SEIRS models for diseases such as malaria in a highly random environment with noises from the disease transmission and natural death rates, and also from the random delays of the incubation and immunity periods. Improved analytical methods and local martingale characterizations are applied to find conditions for the disease to persist near an endemic steady state, and also for the disease to remain permanently in the system over time. Moreover, the ergodic stationary distribution for the stochastic process describing the disease dynamics is defined, and the statistical characteristics of the distribution are given numerically. The results of this study show that the disease will persist and become permanent in the system, regardless of (1) whether the noises are from the disease transmission rate and/or from the natural death rates or (2) whether the delays in the system are constant or random for individuals in the system. Furthermore, it is shown that “weak” noise is associated with the existence of an endemic stationary distribution for the disease, while “strong” noise is associated with extinction of the population over time. Numerical simulation examples for Plasmodium vivax malaria are given.

Mapping malaria seasonality in Madagascar using health facility data

BACKGROUND

Many malaria-endemic areas experience seasonal fluctuations in case incidence as Anopheles mosquito and Plasmodium parasite life cycles respond to changing environmental conditions. Identifying location-specific seasonality characteristics is useful for planning interventions. While most existing maps of malaria seasonality use fixed thresholds of rainfall, temperature, and/or vegetation indices to identify suitable transmission months, we construct a statistical modelling framework for characterising the seasonal patterns derived directly from monthly health facility data.

METHODS

With data from 2669 of the 3247 health facilities in Madagascar, a spatiotemporal regression model was used to estimate seasonal patterns across the island. In the absence of catchment population estimates or the ability to aggregate to the district level, this focused on the monthly proportions of total annual cases by health facility level. The model was informed by dynamic environmental covariates known to directly influence seasonal malaria trends. To identify operationally relevant characteristics such as the transmission start months and associated uncertainty measures, an algorithm was developed and applied to model realisations. A seasonality index was used to incorporate burden information from household prevalence surveys and summarise 'how seasonal' locations are relative to their surroundings.

RESULTS

Positive associations were detected between monthly case proportions and temporally lagged covariates of rainfall and temperature suitability. Consistent with the existing literature, model estimates indicate that while most parts of Madagascar experience peaks in malaria transmission near March-April, the eastern coast experiences an earlier peak around February. Transmission was estimated to start in southeast districts before southwest districts, suggesting that indoor residual spraying should be completed in the same order. In regions where the data suggested conflicting seasonal signals or two transmission seasons, estimates of seasonal features had larger deviations and therefore less certainty.

CONCLUSIONS

Monthly health facility data can be used to establish seasonal patterns in malaria burden and augment the information provided by household prevalence surveys. The proposed modelling framework allows for evidence-based and cohesive inferences on location-specific seasonal characteristics. As health surveillance systems continue to improve, it is hoped that more of such data will be available to improve our understanding and planning of intervention strategies.

Increased Risk of Malaria During the First Year of Life in Small-for-Gestational-Age Infants: A Longitudinal Study in Benin

BACKGROUND

According to the Developmental Origins of Health and Diseases paradigm, the fetal period is highly vulnerable and may have profound effects on later health. Few studies assessed the effect of small-for-gestational age (SGA), a proxy for fetal growth impairment, on risk of malaria during infancy in Africa.

METHODS

We used data from a cohort of 398 mother-child pairs, followed from early pregnancy to age 1 year in Benin. Malaria was actively and passively screened using thick blood smear. We assessed the effect of SGA on risk of malaria infection and clinical malaria from birth to 12 months, after stratifying on the infant's age using a logistic mixed regression model.

RESULTS

After adjustment for potential confounding factors and infant's exposure to mosquitoes, SGA was associated with a 2-times higher risk of malaria infection (adjusted odds ratio [aOR] = 2.16; 95% confidence interval [CI], 1.04-4.51; P = .039) and clinical malaria (aOR = 2.33; 95% CI, 1.09-4.98; P = .030) after age 6 months.

CONCLUSION

Results suggest higher risk of malaria during the second semester of life in SGA infants, and argue for better follow-up of these infants after birth, as currently for preterm babies.

A novel model for malaria prediction based on ensemble algorithms

BACKGROUND AND OBJECTIVE

Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction.

METHODS

The ARIMA, STL+ARIMA, BP-ANN and LSTM network models were separately applied in simulations using malaria data and meteorological data in Yunnan Province from 2011 to 2017. We compared the predictive performance of each model through evaluation measures: RMSE, MASE, MAD. In addition, gradient-boosting regression trees (GBRTs) were used to combine the above four models. We also determined whether stacking structure improved the model prediction performance.

RESULTS

The root mean square errors (RMSEs) of the four sub-models were 13.176, 14.543, 9.571 and 7.208; the mean absolute scaled errors (MASEs) were 0.469, 0.472, 0.296 and 0.266 and the mean absolute deviation (MAD) were 6.403, 7.658, 5.871 and 5.691. After using the stacking architecture combined with the above four models, the RMSE, MASE and MAD values of the ensemble model decreased to 6.810, 0.224 and 4.625, respectively.

CONCLUSIONS

A novel ensemble model based on the robustness of structured prediction and model combination through stacking was developed. The findings suggest that the predictive performance of the final model is superior to that of the other four sub-models, indicating that stacking architecture may have significant implications in infectious disease prediction.

Field Relevant Variation in Ambient Temperature Modifies Density-Dependent Establishment of Plasmodium falciparum Gametocytes in Mosquitoes

The relationship between Plasmodium falciparum gametocyte density and infections in mosquitoes is central to understanding the rates of transmission with important implications for control. Here, we determined whether field relevant variation in environmental temperature could also modulate this relationship. Anopheles stephensi were challenged with three densities of P. falciparum gametocytes spanning a ~10-fold gradient, and housed under diurnal/daily temperature range ("DTR") of 9°C (+5°C and -4°C) around means of 20, 24, and 28°C. Vector competence was quantified as the proportion of mosquitoes infected with oocysts in the midguts (oocyst rates) or infectious with sporozoites in the salivary glands (sporozoite rates) at peak periods of infection for each temperature to account for the differences in development rates. In addition, oocyst intensities were also recorded from infected midguts and the overall study replicated across three separate parasite cultures and mosquito cohorts. While vector competence was similar at 20 DTR 9°C and 24 DTR 9°C, oocyst and sporozoite rates were also comparable, with evidence, surprisingly, for higher vector competence in mosquitoes challenged with intermediate gametocyte densities. For the same gametocyte densities however, severe reductions in the sporozoite rates was accompanied by a significant decline in overall vector competence at 28 DTR 9°C, with gametocyte density per se showing a positive and linear effect at this temperature. Unlike vector competence, oocyst intensities decreased with increasing temperatures with a predominantly positive and linear association with gametocyte density, especially at 28 DTR 9°C. Oocyst intensities across individual infected midguts suggested temperature-specific differences in mosquito susceptibility/resistance: at 20 DTR 9°C and 24 DTR 9°C, dispersion (aggregation) increased in a density-dependent manner but not at 28 DTR 9°C where the distributions were consistently random. Limitations notwithstanding, our results suggest that variation in temperature could modify seasonal dynamics of infectious reservoirs with implications for the design and deployment of transmission-blocking vaccines/drugs.

Modelling competition between hybridising subspecies

The geographic niches of many species are dramatically changing as a result of environmental and anthropogenic impacts such as global climate change and the introduction of invasive species. In particular, genetically compatible subspecies that were once geographically separated are being reintroduced to one another. This is of concern for conservation, where rare or threatened subspecies could be bred out by hybridising with their more common relatives, and for commercial interests, where the stock or quality of desirable harvested species could be compromised. It is also relevant to disease ecology, where disease transmission is heterogeneous among subspecies and hybridisation may affect the rate and spatial spread of disease. We develop and investigate a mathematical model to combine competitive effects via the Lotka-Volterra model with hybridisation effects via mate choice. The species complex is structured into two classes: a subspecies of interest (named x), and other subspecies including any hybrids produced (named y). We show that in the absence of limit cycles the model has four possible equilibrium outcomes, representing every combination: total extinction, x-dominance (y extinct), y-dominance (x extinct), and at most a single coexistence equilibrium. We give conditions for which limit cycles cannot exist, then further show that the "total extinction" equilibrium is always unstable, that y-dominance is always stable, and that the other equilibria have stability depending on the model parameters. We demonstrate that both x-dominance and coexistence are achievable under a wide range of parameter values and initial conditions, which corresponds with empirical evidence of known competing-hybridising systems. We then briefly examine bifurcation behaviour. In particular, we note that a subcritical bifurcation is possible in which a "catastrophic" transition from x-dominance to y-dominance can occur, representing an invasion event. Finally, we briefly examine the common complication of time-varying carrying capacity, showing that such a case can make coexistence more likely.

Sporogony of four Haemoproteus species (Haemosporida: Haemoproteidae), with report of in vitro ookinetes of Haemoproteus hirundinis: phylogenetic inference indicates patterns of haemosporidian parasite ookinete development

BACKGROUND

Haemoproteus (Parahaemoproteus) species (Haemoproteidae) are widespread blood parasites that can cause disease in birds, but information about their vector species, sporogonic development and transmission remain fragmentary. This study aimed to investigate the complete sporogonic development of four Haemoproteus species in Culicoides nubeculosus and to test if phylogenies based on the cytochrome b gene (cytb) reflect patterns of ookinete development in haemosporidian parasites. Additionally, one cytb lineage of Haemoproteus was identified to the species level and the in vitro gametogenesis and ookinete development of Haemoproteus hirundinis was characterised.

METHODS

Laboratory-reared C. nubeculosus were exposed by allowing them to take blood meals on naturally infected birds harbouring single infections of Haemoproteus belopolskyi (cytb lineage hHIICT1), Haemoproteus hirundinis (hDELURB2), Haemoproteus nucleocondensus (hGRW01) and Haemoproteus lanii (hRB1). Infected insects were dissected at intervals in order to detect sporogonic stages. In vitro exflagellation, gametogenesis and ookinete development of H. hirundinis were also investigated. Microscopic examination and PCR-based methods were used to confirm species identity. Bayesian phylogenetic inference was applied to study the relationships among Haemoproteus lineages.

RESULTS

All studied parasites completed sporogony in C. nubeculosus. Ookinetes and sporozoites were found and described. Development of H. hirundinis ookinetes was similar both in vivo and in vitro. Developing ookinetes of this parasite possess long outgrowths, which extend longitudinally and produce the apical end of the ookinetes. A large group of closely related Haemoproteus species with a similar mode of ookinete development was determined. Bayesian analysis indicates that this character has phylogenetic value. The species identity of cytb lineage hDELURB2 was determined: it belongs to H. hirundinis.

CONCLUSIONS

Culicoides nubeculosus is susceptible to and is a likely natural vector of numerous species of Haemoproteus parasites, thus worth attention in haemoproteosis epidemiology research. Data about in vitro development of haemoproteids provide valuable information about the rate of ookinete maturation and are recommended to use as helpful step during vector studies of haemosporidian parasites, particularly because they guide proper dissection interval of infected insects for ookinete detection during in vivo experiments. Additionally, in vitro studies readily identified patterns of morphological ookinete transformations, the characters of which are of phylogenetic value in haemosporidian parasites.

Seasonal patterns of dengue fever in rural Ecuador: 2009-2016

Season is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of dengue fever has been studied in the context of climatic factors, but short- and long-term time trends are less well-understood. With 2009–2016 medical record data from patients diagnosed with dengue fever at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short- and long-term seasonal patterns of dengue fever, as well as the effect of day of the week and public holidays. In a subset analysis, we determined the impact of school schedules on school-aged children. With a separate model, we examined the effect of climate on diagnosis patterns. In the first model, the most important predictors of dengue fever were annual sinusoidal fluctuations in disease, long-term trends (as represented by a spline for the full study duration), day of the week, and hospital. Seasonal trends showed single peaks in case diagnoses, during mid-March. Compared to the average of all days, cases were more likely to be diagnosed on Tuesdays (risk ratio (RR): 1.26, 95% confidence interval (CI) 1.05–1.51) and Thursdays (RR: 1.25, 95% CI 1.02–1.53), and less likely to be diagnosed on Saturdays (RR: 0.81, 95% CI 0.65–1.01) and Sundays (RR: 0.74, 95% CI 0.58–0.95). Public holidays were not significant predictors of dengue fever diagnoses, except for an increase in diagnoses on the day after Christmas (RR: 2.77, 95% CI 1.46–5.24). School schedules did not impact dengue diagnoses in school-aged children. In the climate model, important climate variables included the monthly total precipitation, an interaction between total precipitation and monthly absolute minimum temperature, an interaction between total precipitation and monthly precipitation days, and a three-way interaction between minimum temperature, total precipitation, and precipitation days. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. These results can inform local disease prevention efforts, public health planning, as well as global and regional models of dengue fever trends.

Dengue fever exhibits a seasonal pattern in many parts of the world, much of which has been attributed to climate and weather. However, additional factors may contribute to dengue seasonality. With 2009–2016 medical record data from rural Ecuador, we studied the short- and long-term seasonal patterns of dengue fever, as well as the effect of school schedules and public holidays. We also examined the effect of climate on dengue. We found that dengue diagnoses peak once per year in mid-March, but that diagnoses are also affected by day of the week. Dengue was also impacted by regional climate and complex interactions between local weather variables. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. This is the first report on the impacts of school schedules, holidays, and weekday-weekend patterns on dengue diagnoses. These results suggest a potential impact of human behaviors on dengue exposure risk. More broadly, these results can inform local disease prevention efforts and public health planning, as well as global and regional models of dengue fever trends.

A comparison of thick-film microscopy, rapid diagnostic test, and polymerase chain reaction for accurate diagnosis of Plasmodium falciparum malaria

Background

Accurate diagnosis of malaria is important for effective disease management and control. In Cameroon, presumptive clinical diagnosis, thick-film microscopy (TFM), and rapid diagnostic tests (RDT) are commonly used to diagnose cases of Plasmodium falciparum malaria. However, these methods lack sensitivity to detect low parasitaemia. Polymerase chain reaction (PCR), on the other hand, enhances the detection of sub-microscopic parasitaemia making it a much-needed tool for epidemiological surveys, mass screening, and the assessment of interventions for malaria elimination. Therefore, this study sought to determine the frequency of cases missed by traditional methods that are detected by PCR.

Methods

Blood samples, collected from 551 febrile Cameroonian patients between February 2014 and February 2015, were tested for P. falciparum by microscopy, RDT and PCR. The hospital records of participants were reviewed to obtain data on the clinical diagnosis made by the health care worker.

Results

The prevalence of malaria by microscopy, RDT and PCR was 31%, 45%, and 54%, respectively. However, of the 92% of participants diagnosed as having clinical cases of malaria by the health care worker, 38% were malaria-negative by PCR. PCR detected 23% and 12% more malaria infections than microscopy and RDT, respectively. A total of 128 (23%) individuals had sub-microscopic infections in the study population. The sensitivity of microscopy, RDT, and clinical diagnosis was 57%, 78% and 100%; the specificity was 99%, 94%, and 17%; the positive predictive values were 99%, 94%, and 59%; the negative predictive values were 66%, 78%, and 100%, respectively. Thus, 41% of the participants clinically diagnosed as having malaria had fever caused by other pathogens.

Conclusions

Malaria diagnostic methods, such as TFM and RDT missed 12–23% of malaria cases detected by PCR. Therefore, traditional diagnostic approaches (TFM, RDT and clinical diagnosis) are not adequate when accurate epidemiological data are needed for monitoring malaria control and elimination interventions.

Investigating the upsurge of malaria prevalence in Zambia between 2010 and 2015: a decomposition of determinants

Background

Malaria is among the top causes of mortality and morbidity in Zambia. Efforts to control, prevent, and eliminate it have been intensified in the past two decades which has contributed to reductions in malaria prevalence and under-five mortality. However, there was a 21% upsurge in malaria prevalence between 2010 and 2015. Zambia is one of the only 13 countries to record an increase in malaria among 91 countries monitored by the World Health Organization in 2015. This study investigated the upsurge by decomposition of drivers of malaria.

Methods

The study used secondary data from three waves of nationally representative cross-sectional surveys on key malaria indicators conducted in 2010, 2012 and 2015. Using multivariable logistic regression, determinants of malaria prevalence were identified and then marginal effects of each determinant were derived. The marginal effects were then combined with changes in coverage rates of determinants between 2010 and 2015 to obtain the magnitude of how much each variable contributed to the change in the malaria prevalence.

Results

The odds ratio of malaria for those who slept under an insecticide-treated net (ITN) was 0.90 (95% CI 0.77–0.97), indoor residual spraying (IRS) was 0.66 (95% CI 0.49–0.89), urban residence was 0.23 (95% CI 0.15–0.37), standard house was 0.40 (95% CI 0.35–0.71) and age group 12–59 Months against those below 12 months was 4.04 (95% CI 2.80–5.81). Decomposition of prevalence changes by determinants showed that IRS reduced malaria prevalence by − 0.3% and ITNs by − 0.2% however, these reductions were overridden by increases in prevalence due to increases in the proportion of more at-risk children aged 12–59 months by + 2.3% and rural residents by + 2.2%.

Conclusion

The increases in interventions, such as ITNs and IRS, were shown to have contributed to malaria reduction in 2015; however, changes in demographics such as increases in the proportion of more at risk groups among under-five children and rural residents may have overridden the impact of these interventions and resulted in an overall increase. The upsurge in malaria in 2015 compared to 2010 may not have been due to weaknesses in programme interventions but due to increases in more at-risk children and rural residents compared to 2010. The apparent increase in rural residents in the sample population may not have been a true reflection of the population structure but due to oversampling in rural areas which was not fully adjusted for. The increase in malaria prevalence may therefore have been overestimated.

Uncertainty in malaria simulations in the highlands of Kenya: Relative contributions of model parameter setting, driving climate and initial condition errors

In this study, experiments are conducted to gauge the relative importance of model, initial condition, and driving climate uncertainty for simulations of malaria transmission at a highland plantation in Kericho, Kenya. A genetic algorithm calibrates each of these three factors within their assessed prior uncertainty in turn to see which allows the best fit to a timeseries of confirmed cases. It is shown that for high altitude locations close to the threshold for transmission, the spatial representativeness uncertainty for climate, in particular temperature, dominates the uncertainty due to model parameter settings. Initial condition uncertainty plays little role after the first two years, and is thus important in the early warning system context, but negligible for decadal and climate change investigations. Thus, while reducing uncertainty in the model parameters would improve the quality of the simulations, the uncertainty in the temperature driving data is critical. It is emphasized that this result is a function of the mean climate of the location itself, and it is shown that model uncertainty would be relatively more important at warmer, lower altitude locations.

Climatic drivers of melioidosis in Laos and Cambodia: a 16-year case series analysis

BACKGROUND

Burkholderia pseudomallei is the cause of melioidosis, a serious and difficult to treat infection that is endemic throughout the tropics. Melioidosis incidence is highly seasonal. We aimed to identify the climatic drivers of infection and to shed light on modes of transmission and potential preventive strategies.

METHODS

We examined the records of patients diagnosed with melioidosis at the Microbiology Laboratory of Mahosot Hospital in Vientiane, Laos, between October, 1999, and August, 2015, and all patients with culture-confirmed melioidosis presenting to the Angkor Hospital for Children in Siem Reap, Cambodia, between February, 2009, and December, 2013. We also examined local temperature, humidity, precipitation, visibility, and wind data for the corresponding time periods. We estimated the B pseudomallei incubation period by examining profile likelihoods for hypothetical exposure-to-presentation delays.

FINDINGS

870 patients were diagnosed with melioidosis in Laos and 173 patients were diagnosed with melioidosis in Cambodia during the study periods. Melioidosis cases were significantly associated with humidity (p<0·0001), low visibility (p<0·0001), and maximum wind speeds (p<0·0001) in Laos, and humidity (p=0·010), rainy days (p=0·015), and maximum wind speed (p=0·0070) in Cambodia. Compared with adults, children were at significantly higher odds of infection during highly humid months (odds ratio 2·79, 95% CI 1·83-4·26). Lung and disseminated infections were more common during windy months. The maximum likelihood estimate of the incubation period was 1 week (95% CI 0-2).

INTERPRETATION

The results of this study demonstrate a significant seasonal burden of melioidosis among adults and children in Laos and Cambodia. Our findings highlight the risks of infection during highly humid and windy conditions, and suggest a need for increased awareness among at-risk individuals, such as children.

FUNDING

Wellcome Trust.

Seasonal variations in Plasmodium falciparum parasite prevalence assessed by varying diagnostic tests in asymptomatic children in southern Ghana

Plasmodium falciparum infections presenting either as symptomatic or asymptomatic may contain sexual stage parasites (gametocytes) that are crucial to malaria transmission. In this study, the prevalence of microscopic and submicroscopic asexual and gametocyte parasite stages were assessed in asymptomatic children from two communities in southern Ghana. Eighty children aged twelve years and below, none of whom exhibited signs of clinical malaria living in Obom and Cape Coast were sampled twice, one during the rainy (July 2015) and subsequently during the dry (January 2016) season. Venous blood was used to prepare thick and thin blood smears, spot a rapid malaria diagnostic test (PfHRP2 RDT) as well as prepare filter paper blood spots. Blood cell pellets were preserved in Trizol for RNA extraction. Polymerase chain reaction (PCR) and semi-quantitative real time reverse transcriptase PCR (qRT-PCR) were used to determine submicroscopic parasite prevalence. In both sites 87% (95% CI: 78-96) of the asymptomatic individuals surveyed were parasites positive during the 6 month study period. The prevalence of asexual and gametocyte stage parasites in the rainy season were both significantly higher in Obom than in Cape Coast (P < 0.001). Submicroscopic gametocyte prevalence was highest in the rainy season in Obom but in the dry season in Cape Coast. Parasite prevalence determined by PCR was similar to that determined by qRT-PCR in Obom but significantly lower than that determined by qRT-PCR in Cape Coast. Communities with varying parasite prevalence exhibit seasonal variations in the prevalence of gametocyte carriers. Submicroscopic asymptomatic parasite and gametocyte carriage is very high in southern Ghana, even during the dry season in communities with low microscopic parasite prevalence and likely to be missed during national surveillance exercises.

The use of driving endonuclease genes to suppress mosquito vectors of malaria in temporally variable environments

Background

The use of gene drive systems to manipulate populations of malaria vectors is currently being investigated as a method of malaria control. One potential system uses driving endonuclease genes (DEGs) to spread genes that impose a genetic load. Previously, models have shown that the introduction of DEG-bearing mosquitoes could suppress or even extinguish vector populations in spatially-heterogeneous environments which were constant over time. In this study, a stochastic spatially-explicit model of mosquito ecology is combined with a rainfall model which enables the generation of a variety of daily precipitation patterns. The model is then used to investigate how releases of a DEG that cause a bias in population sex ratios towards males are affected by seasonal or random rainfall patterns. The parameters of the rainfall model are then fitted using data from Bamako, Mali, and Mbita, Kenya, to evaluate release strategies in similar climatic conditions.

Results

In landscapes with abundant resources and large mosquito populations the spread of a DEG is reliable, irrespective of variability in rainfall. This study thus focuses mainly on landscapes with low density mosquito populations where the spread of a DEG may be sensitive to variation in rainfall. It is found that an introduced DEG will spread into its target population more reliably in wet conditions, yet an established DEG will have more impact in dry conditions. In strongly seasonal environments, it is thus preferable to release DEGs at the onset of a wet season to maximize their spread before the following dry season. If the variability in rainfall has a substantial random component, there is a net increase in the probability that a DEG release will lead to population extinction, due to the increased impact of a DEG which manages to establish in these conditions. For Bamako, where annual rainfall patterns are characterized by a long dry season, it is optimal to release a DEG at the start of the wet season, where the population is growing fastest. By contrast release timing is of lower importance for the less seasonal Mbita.

Conclusion

This analysis suggests that DEG based methods of malaria vector control can be effective in a wide range of climates. In environments with substantial temporal variation in rainfall, careful timing of releases which accounts for the temporal variation in population density can substantially improve the probability of mosquito suppression or extinction.

Electronic supplementary material

The online version of this article (10.1186/s12936-018-2259-8) contains supplementary material, which is available to authorized users.

The associations between malaria, interventions, and the environment: a systematic review and meta-analysis

BACKGROUND

Malaria transmission is driven by multiple factors, including complex and multifaceted connections between malaria transmission, socioeconomic conditions, climate and interventions. Forecasting models should account for all significant drivers of malaria incidence although it is first necessary to understand the relationship between malaria burden and the various determinants of risk to inform the development of forecasting models. In this study, the associations between malaria risk, environmental factors, and interventions were evaluated through a systematic review.

METHODS

Five electronic databases (CAB Abstracts, EMBASE, Global Health, MEDLINE and ProQuest Dissertations & Theses) were searched for studies that included both the effects of the environment and interventions on malaria within the same statistical model. Studies were restricted to quantitative analyses and health outcomes of malaria mortality or morbidity, outbreaks, or transmission suitability. Meta-analyses were conducted on a subset of results using random-effects models.

RESULTS

Eleven studies of 2248 potentially relevant articles that met inclusion criteria were identified for the systematic review and two meta-analyses based upon five results each were performed. Normalized Difference Vegetation Index was not found to be statistically significant associated with malaria with a pooled OR of 1.10 (95% CI 0.07, 1.71). Bed net ownership was statistically associated with decreasing risk of malaria, when controlling for the effects of environment with a pooled OR of 0.75 (95% CI 0.60, 0.95). In general, environmental effects on malaria, while controlling for the effect of interventions, were variable and showed no particular pattern. Bed nets ownership, use and distribution, have a significant protective effect while controlling for environmental variables.

CONCLUSIONS

There are a limited number of studies which have simultaneously evaluated both environmental and interventional effects on malaria risk. Poor statistical reporting and a lack of common metrics were important challenges for this review, which must be addressed to ensure reproducibility and quality research. A comprehensive or inclusive approach to identifying malaria determinants using standardized indicators would allow for a better understanding of its epidemiology, which is crucial to improve future malaria risk estimations.

Seasonal and inter-annual variation of malaria parasite detection in wild chimpanzees

BACKGROUND

Cross-sectional surveys of chimpanzee (Pan troglodytes) communities across sub-Saharan Africa show large geographical variation in malaria parasite (Plasmodium spp.) prevalence. The drivers leading to this apparent spatial heterogeneity may also be temporally dynamic but data on prevalence variation over time are missing for wild great apes. This study aims to fill this fundamental gap.

METHODS

Some 681 faecal samples were collected from 48 individuals of a group of habituated chimpanzees (Taï National Park, Côte d'Ivoire) across four non-consecutive sampling periods between 2005 and 2015.

RESULTS

Overall, 89 samples (13%) were PCR-positive for malaria parasite DNA. The proportion of positive samples ranged from 0 to 43% per month and 4 to 27% per sampling period. Generalized Linear Mixed Models detected significant seasonal and inter-annual variation, with seasonal increases during the wet seasons and apparently stochastic inter-annual variation. Younger individuals were also significantly more likely to test positive.

CONCLUSIONS

These results highlight strong temporal fluctuations of malaria parasite detection rates in wild chimpanzees. They suggest that the identification of other drivers of malaria parasite prevalence will require longitudinal approaches and caution against purely cross-sectional studies, which may oversimplify the dynamics of this host-parasite system.

Association between malaria incidence and meteorological factors: a multi-location study in China, 2005-2012

This study aims to investigate the climate-malaria associations in nine cities selected from malaria high-risk areas in China. Daily reports of malaria cases in Anhui, Henan, and Yunnan Provinces for 2005-2012 were obtained from the Chinese Center for Disease Control and Prevention. Generalized estimating equation models were used to quantify the city-specific climate-malaria associations. Multivariate random-effects meta-regression analyses were used to pool the city-specific effects. An inverted-U-shaped curve relationship was observed between temperatures, average relative humidity, and malaria. A 1 °C increase of maximum temperature (T max) resulted in 6·7% (95% CI 4·6-8·8%) to 15·8% (95% CI 14·1-17·4%) increase of malaria, with corresponding lags ranging from 7 to 45 days. For minimum temperature (T min), the effect estimates peaked at lag 0 to 40 days, ranging from 5·3% (95% CI 4·4-6·2%) to 17·9% (95% CI 15·6-20·1%). Malaria is more sensitive to T min in cool climates and T max in warm climates. The duration of lag effect in a cool climate zone is longer than that in a warm climate zone. Lagged effects did not vanish after an epidemic season but waned gradually in the following 2-3 warm seasons. A warming climate may potentially increase the risk of malaria resurgence in China.

Seasonal Dynamics of Malaria in Pregnancy in West Africa: Evidence for Carriage of Infections Acquired Before Pregnancy Until First Contact with Antenatal Care

In malaria-endemic areas, Plasmodium falciparum prevalence is often high in young women because of 1) low use of insecticide-treated nets before their first pregnancy and 2) acquired immunity, meaning infections are asymptomatic and thus untreated. Consequently, a common source of malaria in pregnancy (MiP) may be infected women becoming pregnant, rather than pregnant women becoming infected. In this study, prevalence of infection was determined by microscopy at first antenatal care (ANC) visit in primigravidae and secundigravidae in Ghana, Burkina Faso, Mali, and The Gambia, four countries with strong seasonal variations in transmission. Duration of pregnancy spent in the rainy season and other risk factors for infection were evaluated using multivariable Poisson regression. We found that the overall prevalence of malaria at first ANC was generally high and increased with time spent pregnant during the rainy season: prevalence among those with the longest exposure was 59.7% in Ghana, 56.7% in Burkina Faso, 42.2% in Mali, and 16.8% in Gambia. However, the prevalence was substantial even among women whose entire pregnancy before first ANC had occurred in the dry season: 41.3%, 34.4%, 11.5%, and 7.8%, respectively, in the four countries. In multivariable analysis, risk of infection was also higher among primigravidae, younger women, and those of lower socioeconomic status, independent of seasonality. High prevalence among women without exposure to high transmission during their pregnancy suggests that part of the MiP burden results from long-duration infections, including those acquired preconception. Prevention of malaria before pregnancy is needed to reduce the MiP burden.

Temporal variation in confirmed diagnosis of fever-related malarial cases among children under-5 years by community health workers and in health facilities between years 2013 and 2015 in Siaya County, Kenya

BACKGROUND

Malaria case management continues to experience dynamic changes. Building community capacity is instrumental in both prevention and treatment of malaria. The World Health Organization (WHO) recommends utilization of well-trained and supervised community health workers (CHWs) to reduce the burden of malaria deaths among children under-5 years of age in Africa. Longitudinally-tracked information on utilization of CHWs by communities in terms of trends in diagnosis of malaria in children under-5 years of age is essential in influencing national and local malaria control policies and strategies.

METHODS

A desktop review was carried out of a database consisting of confirmed uncomplicated malaria cases in 10 villages using CHWs and out-patient departments of 10 health facilities in children under-five for the period of 3 years between January 2013 and December 2015. Analyses of association between the diagnosed cases and satellite-based rainfall, village and time (months and years) were carried out using a Poisson regression model.

RESULTS

Analysis of malaria diagnoses made by CHWs showed the following trends: (i) the incidence of reported documented malaria-positive fever cases increased with time (2013-2015) and the difference over the years was statistically significant (P < 0.001), (ii) specific village was significantly associated (P < 0.001) with reporting malaria-positive fever cases, (iii) the long-term monthly sequence starting from highest to lowest incidence of reported malaria-positive fever cases was July, May and June, March, August, April, September, November, and February, October and, finally, January, and the difference in reported malaria-positives between the months was statistically significant (P = 0.001) and (iv) none of the tested rainfall regimes (current, lagged or cumulative) was associated with reported malaria-positive fever cases during the 3-year period (P > 0.1). Looking at the number of diagnoses made at the health facilities, (i) The number of reported malaria-positive fever cases decreased with time (2013-2015) and the difference among the years was not statistically significant (P = 0.399), (ii) The long-term monthly sequence starting from highest to lowest number of reported malaria-positive fever cases was July, June, May, April, January, August, March, February, September, November, October and December, and the difference between the months was statistically significant (P < 0.001).

CONCLUSIONS

CHWs have the potential to play a major role in diagnosing and treating malaria, thereby decreasing under-five children mortality. Temporally, the risk of diagnosing malaria seems predictable and this may present opportunities for policy-targeted malaria preparedness and control. The findings are expected to support policy actions that may scale-up community health services in remote rural settings.

Geostatistical modelling of malaria indicator survey data to assess the effects of interventions on the geographical distribution of malaria prevalence in children less than 5 years in Uganda

BACKGROUND

Malaria burden in Uganda has declined disproportionately among regions despite overall high intervention coverage across all regions. The Uganda Malaria Indicator Survey (MIS) 2014-15 was the second nationally representative survey conducted to provide estimates of malaria prevalence among children less than 5 years, and to track the progress of control interventions in the country. In this present study, 2014-15 MIS data were analysed to assess intervention effects on malaria prevalence in Uganda among children less than 5 years, assess intervention effects at regional level, and estimate geographical distribution of malaria prevalence in the country.

METHODS

Bayesian geostatistical models with spatially varying coefficients were used to determine the effect of interventions on malaria prevalence at national and regional levels. Spike-and-slab variable selection was used to identify the most important predictors and forms. Bayesian kriging was used to predict malaria prevalence at unsampled locations.

RESULTS

Indoor Residual Spraying (IRS) and Insecticide Treated Nets (ITN) ownership had a significant but varying protective effect on malaria prevalence. However, no effect was observed for Artemisinin Combination-based Therapies (ACTs). Environmental factors, namely, land cover, rainfall, day and night land surface temperature, and area type were significantly associated with malaria prevalence. Malaria prevalence was higher in rural areas, increased with the child's age, and decreased with higher household socioeconomic status and higher level of mother's education. The highest prevalence of malaria in children less than 5 years was predicted for regions of East Central, North East and West Nile, whereas the lowest was predicted in Kampala and South Western regions, and in the mountainous areas in Mid-Western and Mid-Eastern regions.

CONCLUSIONS

IRS and ITN ownership are important interventions against malaria prevalence in children less than 5 years in Uganda. The varying effects of the interventions calls for selective implementation of control tools suitable to regional ecological settings. To further reduce malaria burden and sustain malaria control in Uganda, current tools should be supplemented by health system strengthening, and socio-economic development.

Monitoring of Selected Health Indicators in Children Living in a Copper Mine Development Area in Northwestern Zambia

The epidemiology of malaria, anaemia and malnutrition in children is potentially altered in mining development areas. In a copper extraction project in northwestern Zambia, a health impact assessment (HIA) was commissioned to predict, manage and monitor health impacts. Two cross-sectional surveys were conducted: at baseline prior to project development (2011) and at four years into development (2015). Prevalence of Plasmodium falciparum, anaemia and stunting were assessed in under-five-year-old children, while hookworm infection was assessed in children aged 9–14 years in communities impacted and comparison communities not impacted by the project. P. falciparum prevalence was significantly higher in 2015 compared to 2011 in both impacted and comparison communities (odds ratio (OR) = 2.51 and OR = 6.97, respectively). Stunting was significantly lower in 2015 in impacted communities only (OR = 0.63). Anaemia was slightly lower in 2015 compared to baseline in both impacted and comparison communities. Resettlement due to the project and migration background (i.e., moving into the area within the past five years) were generally associated with better health outcomes in 2015. We conclude that repeated cross-sectional surveys to monitor health in communities impacted by projects should become an integral part of HIA to deepen the understanding of changing patterns of health and support implementation of setting-specific public health measures.

Nutrition Smoothing: Can Proximity to Towns and Cities Protect Rural Children against Seasonal Variation in Agroclimatic Conditions at Birth?

A large literature links early-life environmental shocks to later outcomes. This paper uses seasonal variation across the Democratic Republic of the Congo to test for nutrition smoothing, defined here as attaining similar height, weight and mortality outcomes despite different agroclimatic conditions at birth. We find that gaps between siblings and neighbors born at different times of year are larger in more remote rural areas, farther from the equator where there are greater seasonal differences in rainfall and temperature. For those born at adverse times in places with pronounced seasonality, the gains associated with above-median proximity to nearby towns are similar to rising one quintile in the national distribution of household wealth for mortality, and two quintiles for attained height. Smoothing of outcomes could involve a variety of mechanisms to be addressed in future work, including access to food markets, health services, public assistance and temporary migration to achieve more uniform dietary intake, or less exposure and improved recovery from seasonal diseases.

The geography of imported malaria to non-endemic countries: a meta-analysis of nationally reported statistics

BACKGROUND

Malaria remains a problem for many countries classified as malaria free through cases imported from endemic regions. Imported cases to non-endemic countries often result in delays in diagnosis, are expensive to treat, and can sometimes cause secondary local transmission. The movement of malaria in endemic countries has also contributed to the spread of drug resistance and threatens long-term eradication goals. Here we focused on quantifying the international movements of malaria to improve our understanding of these phenomena and facilitate the design of mitigation strategies.

METHODS

In this meta-analysis, we studied the database of publicly available nationally reported statistics on imported malaria in the past 10 years, covering more than 50 000 individual cases. We obtained data from 40 non-endemic countries and recorded the geographical variations.

FINDINGS

Infection movements were strongly skewed towards a small number of high-traffic routes between 2005 and 2015, with the west Africa region accounting for 56% (13 947/24 941) of all imported cases to non-endemic countries with a reported travel destination, and France and the UK receiving the highest number of cases, with more than 4000 reported cases per year on average. Countries strongly linked by movements of imported cases are grouped by historical, language, and travel ties. There is strong spatial clustering of plasmodium species types.

INTERPRETATION

The architecture of the air network, historical ties, demographics of travellers, and malaria endemicity contribute to highly heterogeneous patterns of numbers, routes, and species compositions of parasites transported. With global malaria eradication on the international agenda, malaria control altering local transmission, and the threat of drug resistance, understanding these patterns and their drivers is increasing in importance.

FUNDING

Bill & Melinda Gates Foundation, National Institutes of Health, UK Medical Research Council, UK Department for International Development, Wellcome Trust.

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence

Background

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region.

Methods

This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models.

Results

Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts.

Conclusion

Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1602-1) contains supplementary material, which is available to authorized users.

Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria

Background

The serial interval is a fundamentally important quantity in infectious disease epidemiology that has numerous applications to inferring patterns of transmission from case data. Many of these applications are apropos of efforts to eliminate falciparum malaria from locations throughout the world, yet the serial interval for this disease is poorly understood quantitatively.

Methods

To obtain a quantitative estimate of the serial interval for falciparum malaria, the sum of the components of the falciparum malaria transmission cycle was taken based on a combination of mathematical models and empirical data. During this process, a number of factors were identified that account for substantial variability in the serial interval across different contexts.

Results

Treatment with anti-malarial drugs roughly halves the serial interval due to an abbreviated period of human infectiousness, seasonality results in different serial intervals at different points in the transmission season, and variability in within-host dynamics results in many individuals whose serial intervals do not follow average behaviour. Furthermore, 24.5 % of secondary cases presenting clinically did so prior to the primary cases being identified through active detection of infection.

Conclusions

These results have important implications for epidemiological applications that rely on quantitative estimates of the serial interval of falciparum malaria and other diseases characterized by prolonged infections and complex ecological drivers.

Epidemiology and clinical features of imported malaria in East London

BACKGROUND

Malaria is the most common imported tropical disease in the United Kingdom (UK). The overall mortality is low but inter-regional differences have been observed.

METHODS

We conducted a 2-year retrospective review of clinical and laboratory records of patients with malaria attending three acute hospitals in East London from 1 April 2013 through 31 March 2015. Epidemiological and clinical characteristics of imported malaria were described and risk factors associated with severe falciparum malaria were explored.

RESULTS

In total, 133 patients with laboratory-confirmed malaria were identified including three requiring critical care admission but no deaths. The median age at presentation was 41 years (IQR 30-50). The majority of patients were males (64.7%, 86/133) and had Black or Black British ethnicity (67.5%, 79/117). West Africa was the most frequent region of travel (70.4%, 76/108). Chemoprophylaxis use was poor (25.3%, 20/79). The interval between arriving in the UK and presenting to hospital was short (median 10 days; IQR 5-15.5, n = 84). July-September was the peak season of presentation (34.6%, 46/133). Plasmodium falciparum was the commonest species (76.7%, 102/133) and 31.4% (32/102) of these patients had parasitaemia >2%. Severe falciparum malaria was documented in 36.3% (37/102) of patients and the October-March season presentation was associated with an increased risk of severity (OR 3.00; 95% CI 1.30-6.93). Black patients appeared to have reduced risk of severe falciparum malaria (OR 0.46; 95% CI 0.16-1.35) but this was not statistically significant. HIV sero-status was determined in only 27.1% (36/133) of cases. Only 8.5% (10/117) of all malaria patients were treated as outpatients.

CONCLUSION

Clinicians need to raise awareness on malaria prevention strategies, improve rates of HIV testing in tropical travellers, and familiarise themselves with ambulatory management of malaria. The relationship between season of presentation, ethnicity and severity of falciparum malaria should be explored further.

A geostatistical analysis of the association between armed conflicts and Plasmodium falciparum malaria in Africa, 1997-2010

BACKGROUND

The absence of conflict in a country has been cited as a crucial factor affecting the operational feasibility of achieving malaria control and elimination, yet mixed evidence exists on the influence that conflicts have had on malaria transmission. Over the past two decades, Africa has seen substantial numbers of armed conflicts of varying length and scale, creating conditions that can disrupt control efforts and impact malaria transmission. However, very few studies have quantitatively assessed the associations between conflicts and malaria transmission, particularly in a consistent way across multiple countries.

METHODS

In this analysis an explicit geostatistical, autoregressive, mixed model is employed to quantitatively assess the association between conflicts and variations in Plasmodium falciparum parasite prevalence across a 13-year period in sub-Saharan Africa.

RESULTS

Analyses of geolocated, malaria prevalence survey variations against armed conflict data in general showed a wide, but short-lived impact of conflict events geographically. The number of countries with decreased P. falciparum parasite prevalence (17) is larger than the number of countries with increased transmission (12), and notably, some of the countries with the highest transmission pre-conflict were still found with lower transmission post-conflict. For four countries, there were no significant changes in parasite prevalence. Finally, distance from conflicts, duration of conflicts, violence of conflict, and number of conflicts were significant components in the model explaining the changes in P. falciparum parasite rate.

CONCLUSIONS

The results suggest that the maintenance of intervention coverage and provision of healthcare in conflict situations to protect vulnerable populations can maintain gains in even the most difficult of circumstances, and that conflict does not represent a substantial barrier to elimination goals.