Modelling the incidence of Plasmodium vivax and Plasmodium falciparum malaria in Afghanistan 2006-2009

Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention

Background

The spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies.

Methods

This study developed intelligent data analytics incorporating Bayesian trend and spatio-temporal Integrated Laplace Approximation models to analyse high-burden over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015.

Results

The results show that at least 5.4 million people live in catchment areas with increasing trends of malaria, covering over 47% of all health facilities, while 5.7 million people live in areas with a declining trend (95% CI), covering 27% of health facilities. A two-scale spatio-temporal trend comparison identified significant differences between health facilities and higher-level districts, and the pattern observed in the southeastern region of Zambia provides the first evidence of the impact of recently implemented localised interventions.

Conclusions

The results support our recommendation for an adaptive scaling approach when implementing national malaria monitoring, control and elimination strategies and a particular need for stratified subnational approaches targeting high-burden regions with increasing disease trends. Strong clusters along borders with highly endemic countries in the north and south of Zambia underscore the need for coordinated cross-border malaria initiatives and strategies.

Malaria is an infectious disease that is widespread in many African countries. Malaria transmission within a country can vary between regions, so tailored interventions for malaria control and elimination targeted to different regions are necessary. To achieve this, it is important to measure and monitor the frequency of malaria infections, its risk, and trends at different geographic administrative scales. This study analysed over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. The results showed an increasing national trend in malaria risk and malaria infection frequency and identified differences between health facility and district trends. These findings support a flexible approach when implementing and expanding national malaria monitoring, control and elimination strategies, especially in areas bordering countries where malaria is widespread, cross-border movement is common, and cross-border initiatives could be beneficial.

Lubinda et al. analyse over 32 million health-facility reported malaria cases in Zambia (2009–15) to examine spatially-structured temporal trends. They observe overall increasing trends in risk and rates and highlight the potential benefits of using an adaptive scaling approach in national malaria strategies, and a need for cross-border initiatives.

Prediction of congenital heart disease for newborns: comparative analysis of Holt-Winters exponential smoothing and autoregressive integrated moving average models

OBJECTIVE

To describe the temporal trend of the number of new congenital heart disease (CHD) cases among newborns in Jinhua from 2019 to 2020 and explored an appropriate model to fit and forecast the tendency of CHD.

METHODS

Data on CHD from 2019 to 2020 was collected from a health information system. We counted the number of newborns with CHD weekly and separately used the additive Holt-Winters ES method and ARIMA model to fit and predict the number of CHD for newborns in Jinhua. By comparing the mean square error, rooted mean square error and mean absolute percentage error of each approach, we evaluated the effects of different approaches for predicting the number of CHD in newborns.

RESULTS

A total of 1135 newborns, including 601 baby girls and 534 baby boys, were admitted for CHD from HIS in Jinhua during the 2-year study period. The prevalence of CHD among newborns in Jinhua in 2019 was 0.96%. Atrial septal defect was diagnosed the most frequently among all newborns with CHD. The number of CHD cases among newborns remained stable in 2019 and 2020. There were fewer cases in spring and summer, while cases peaked in November and December. The ARIMA(2,1,1) model relatively offered advantages over the additive Holt-winters ES method in predicting the number of newborns with CHD, while the accuracy of ARIMA(2,1,1) was not very ideal.

CONCLUSIONS

The diagnosis of CHD is related to many risk factors, therefore, when using temporal models to fit and predict the data, we must consider such factors' influence and try to incorporate them into the models.

Molecular Detection of Plasmodium Infection among Anophelinae Mosquitoes and Differentiation of Biological Forms of Anopheles Stephensi Collected from Malarious Areas of Afghanistan and Iran

Background

Updated information on the vectorial capacity of vectors is required in each malarious areas as well in Iran and its neighboring countries such as Afghanistan. The aims of this study were to investigate the potential infection of about 800 specimens collected from malarious areas of Afghanistan and Iran, and to differentiate biological forms of Anopheles stephensi.

Method

Two molecular markers, 18S RNA gene subunit and AsteObp1 intron I, were used respectively for investigation Plasmodium infection and identifying the biological forms of An. stephensi.

Results

Plasmodium infection was detected in 4 pools of Afghanistan specimens, including An. stephensi, collected from Nangarhar. Individually examination showed infection in 5 An. stephensi (infection rate: 1.25), to P. falciparum (2), P. vivax (2) and a mix infection. Out of five infected specimens, three were intermediate forms and two were mysorensis. No infection was found in specimens collected from Iran (Chabahar County), probably due to the active malaria control program in south-east of Iran.

Conclusion

The key role of An. stephensi, as a known Asian malaria vector, was re-emphasized in Afghanistan by the results achieved here. The fauna of vectors and the pattern of biological forms of An. stephensi are similar in both countries that urge regional investigations to provide evidence-based and applied data for decision-maker in malaria control.

Joint modelling of malaria and anaemia in children less than five years of age in Malawi

BACKGROUND

Malaria and anaemia jointly remain a public health problem in developing countries of which Malawi is one. Although there is an improvement along with intervention strategies in fighting against malaria and anaemia in Malawi, the two diseases remain significant problems, especially in children 6-59 months of age. The main objective of this study was to examine the association between malaria and anaemia. Moreover, the study investigated whether socio-economic, geographic, and demographic factors had a significant impact on malaria and anaemia.

DATA AND METHODOLOGY

The present study used a secondary cross-sectional data set from the 2017 Malawi Malaria Indicator Survey (MMIS) with a total number of 2 724 children 6-9 months of age. The study utilized a multivariate joint model within the ambit of the generalized linear mixed model (GLMM) to analyse the data. The two response variables for this study were: the child has either malaria or anaemia.

RESULTS

The prevalence of malaria was 37.2% of the total number of children who were tested using an RDT, while 56.9% were anaemic. The results from the multivariate joint model under GLMM indicated a positive association between anaemia and malaria. Furthermore, the same results showed that mother's education level, child's age, the altitude of the place of residence, place of residence, toilet facility, access to electricity and children who slept under a mosquito bed net the night before the survey had a significant effect on malaria and anaemia.

CONCLUSION

The study indicated that there is a strong association between anaemia and malaria. This is interpreted to indicate that controlling for malaria can result in a reduction of anaemia. The socio-economic, geographical and demographic variables have a significant effect on improving malaria and anaemia. Thus, improving health care, toilet facilities, access to electricity, especially in rural areas, educating the mothers of children and increasing mosquito bed nets would contribute in the reduction of malaria and anaemia in Malawi.

Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions

Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.

Prevalence of and factors associated with malaria in children under five years of age in Malawi, using malaria indicator survey data

BACKGROUND

Malaria remains a public health problem in developing countries and Malawi is no different. Although there has been an improvement in reducing malaria in Malawi, it remains a problem, especially in children less than five years old. The primary objective of the study was to assess whether socio-economic, geographic and demographic factors are associated with malaria, using the generalized additive mixed model (GAMM).

DATA AND METHODOLOGY

The study used a 2017 dataset from the Malawi Malaria Indicator Survey (MMI) with a total number of 2724 children under five years old. The study also utilized the GAMM to analyze data. The outcome was that either the child had malaria or did not, as detected using the malaria Rapid Diagnostic Test (RDT) (Ayele et al., 2014a).

RESULTS

In this study, more than 37 % of the total number of children who were tested showed a positive malaria result. In addition, the results from this study using GAMM indicated that anaemia, mother's education level, wealth index, child's age, the altitude of the place of residence, region, place of residence, toilet facility and electricity were significantly associated with a positive malaria RDT.

CONCLUSION

The study revealed that socio-economic, geographical and demographic variables are the key factors in improving malaria vectors in children. Improving income levels and supporting the poorer rural community mostly from the Central Region would be a great achievement in reducing malaria vectors in Malawi. In addition, improving health care in rural areas, especially at higher altitudes, would contribute to controlling malaria and reducing anaemia.

Malaria elimination transmission and costing in the Asia-Pacific: a multi-species dynamic transmission model

Background: The Asia-Pacific region has made significant progress in combatting malaria since 2000 and a regional goal for a malaria-free Asia Pacific by 2030 has been recognised at the highest levels. External financing has recently plateaued and with competing health risks, countries face the risk of withdrawal of funding as malaria is perceived as less of a threat. An investment case was developed to provide economic evidence to inform policy and increase sustainable financing. Methods: A dynamic epidemiological-economic model was developed to project rates of decline to elimination by 2030 and determine the costs for elimination in the Asia-Pacific region. The compartmental model was used to capture the dynamics of Plasmodium falciparum and Plasmodium vivax malaria for the 22 countries in the region in a metapopulation framework. This paper presents the model development and epidemiological results of the simulation exercise. Results: The model predicted that all 22 countries could achieve Plasmodium falciparum and Plasmodium vivax elimination by 2030, with the People’s Democratic Republic of China, Sri Lanka and the Republic of Korea predicted to do so without scaling up current interventions. Elimination was predicted to be possible in Bangladesh, Bhutan, Malaysia, Nepal, Philippines, Timor-Leste and Vietnam through an increase in long-lasting insecticidal nets (and/or indoor residual spraying) and health system strengthening, and in the Democratic People’s Republic of Korea, India and Thailand with the addition of innovations in drug therapy and vector control. Elimination was predicted to occur by 2030 in all other countries only through the addition of mass drug administration to scale-up and/or innovative activities. Conclusions: This study predicts that it is possible to have a malaria-free region by 2030. When computed into benefits and costs, the investment case can be used to advocate for sustained financing to realise the goal of malaria elimination in Asia-Pacific by 2030.

Malaria elimination transmission and costing in the Asia-Pacific: a multi-species dynamic transmission model

Background: The Asia-Pacific region has made significant progress in combatting malaria since 2000 and a regional goal for a malaria-free Asia Pacific by 2030 has been recognised at the highest levels. External financing has recently plateaued and with competing health risks, countries face the risk of withdrawal of funding as malaria is perceived as less of a threat. An investment case was developed to provide economic evidence to inform policy and increase sustainable financing. Methods: A dynamic epidemiological-economic model was developed to project rates of decline to elimination by 2030 and determine the costs for elimination in the Asia-Pacific region. The compartmental model was used to capture the dynamics of Plasmodium falciparum and Plasmodium vivax malaria for the 22 countries in the region in a metapopulation framework. This paper presents the model development and epidemiological results of the simulation exercise. Results: The model predicted that all 22 countries could achieve Plasmodium falciparum and Plasmodium vivax elimination by 2030, with the People’s Democratic Republic of China, Sri Lanka and the Republic of Korea predicted to do so without scaling up current interventions. Elimination was predicted to be possible in Bangladesh, Bhutan, Malaysia, Nepal, Philippines, Timor-Leste and Vietnam through an increase in long-lasting insecticidal nets (and/or indoor residual spraying) and health system strengthening, and in the Democratic People’s Republic of Korea, India and Thailand with the addition of innovations in drug therapy and vector control. Elimination was predicted to occur by 2030 in all other countries only through the addition of mass drug administration to scale-up and/or innovative activities. Conclusions: This study predicts that it is possible to have a malaria-free region by 2030. When computed into benefits and costs, the investment case can be used to advocate for sustained financing to realise the goal of malaria elimination in Asia-Pacific by 2030.

Coverage of routine reporting on malaria parasitological testing in Kenya, 2015-2016

BACKGROUND

Following the launch of District Health Information System 2 across facilities in Kenya, more health facilities are now capable of carrying out malaria parasitological testing and reporting data as part of routine health information systems, improving the potential value of routine data for accurate and timely tracking of rapidly changing disease epidemiology at fine spatial resolutions.

OBJECTIVES

This study evaluates the current coverage and completeness of reported malaria parasitological testing data in DHIS2 specifically looking at patterns in geographic coverage of public health facilities in Kenya.

METHODS

Monthly facility level data on malaria parasitological testing were extracted from Kenya DHIS2 between November 2015 and October 2016. DHIS2 public facilities were matched to a geo-coded master facility list to obtain coordinates. Coverage was defined as the geographic distribution of facilities reporting any data by region. Completeness of reporting was defined as the percentage of facilities reporting any data for the whole 12-month period or for 3, 6 and 9 months.

RESULTS

Public health facilities were 5,933 (59%) of 10,090 extracted. Fifty-nine per Cent of the public facilities did not report any data while 36, 29 and 22% facilities had data reported at least 3, 6 and 9 months, respectively. Only 8% of public facilities had data reported for every month. There were proportionately more hospitals (86%) than health centres (76%) and dispensaries/clinics (30%) reporting. There were significant geographic variations in reporting rates. Counties along the malaria endemic coast had the lowest reporting rate with only 1% of facilities reporting consistently for 12 months.

CONCLUSION

Current coverage and completeness of reporting of malaria parasitological diagnosis across Kenya's public health system remains poor. The usefulness of routine data to improve our understanding of sub-national heterogeneity across Kenya would require significant improvements to the consistency and coverage of data captured by DHIS2.

Downscaling incidence risk mapping for a Colombian malaria endemic region

OBJECTIVE

To map at a fine spatial scale, the risk of malaria incidence for the important endemic region is Urabá-Bajo Cauca and Alto Sinú, NW Colombia, using a new modelling framework based on GIS and remotely sensed environmental data.

METHODS

The association between environmental and topographic variables obtained from remote sensors and the annual parasite incidence (API) for the years 2013-2015 was calculated using multiple regression analysis; subsequently, a model was constructed to estimate the API and to project it to the entire endemic region in order to design the risk map. The model was validated by relating the obtained API values with the presence of the three main Colombian malaria vectors, Anopheles darlingi, Anopheles albimanus and Anopheles nuneztovari.

RESULTS

Temperature and Normalized Difference Water Index (NDWI) showed a significant correlation with the observed API. The risk map of malaria incidence showed that the zones at higher risk in the Urabá-Bajo Cauca and Alto Sinú region were located south-east of the region, while the northern area presented the lowest malaria risk. A method was generated to estimate the API for small urban centres, instead of the used reports at the municipality level.

CONCLUSIONS

These results provide evidence of the utility of risk maps to identify environmentally vulnerable areas at a fine spatial resolution in the Urabá-Bajo Cauca and Alto Sinú region. This information contributes to the implementation of vector control interventions at the microgeographic scale at areas of high malaria risk.

malERA: An updated research agenda for combination interventions and modelling in malaria elimination and eradication

This paper summarises key advances and priorities since the 2011 presentation of the Malaria Eradication Research Agenda (malERA), with a focus on the combinations of intervention tools and strategies for elimination and their evaluation using modelling approaches. With an increasing number of countries embarking on malaria elimination programmes, national and local decisions to select combinations of tools and deployment strategies directed at malaria elimination must address rapidly changing transmission patterns across diverse geographic areas. However, not all of these approaches can be systematically evaluated in the field. Thus, there is potential for modelling to investigate appropriate 'packages' of combined interventions that include various forms of vector control, case management, surveillance, and population-based approaches for different settings, particularly at lower transmission levels. Modelling can help prioritise which intervention packages should be tested in field studies, suggest which intervention package should be used at a particular level or stratum of transmission intensity, estimate the risk of resurgence when scaling down specific interventions after local transmission is interrupted, and evaluate the risk and impact of parasite drug resistance and vector insecticide resistance. However, modelling intervention package deployment against a heterogeneous transmission background is a challenge. Further validation of malaria models should be pursued through an iterative process, whereby field data collected with the deployment of intervention packages is used to refine models and make them progressively more relevant for assessing and predicting elimination outcomes.

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya

BACKGROUND

Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors.

METHODS

A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test.

RESULTS

The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R ² = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values.

CONCLUSION

Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

Composition of Anopheles Species Collected from Selected Malarious Areas of Afghanistan and Iran

BACKGROUND

Malarious areas in Iran are close to Afghanistan and Pakistan that urge the researchers to extend their knowledge on malaria epidemiology to the neighboring countries as well. Vectorial capacity differs at species or even at population level, the first essential step is accurate identification of vectors. This study aimed to identify Anopheles species composition in selected malarious areas of Afghanistan and Iran, providing further applied data for other research in two countries.

METHODS

Adults Anopheles spp. were collected from four provinces in Afghanistan (Badakhshan, Herat, Kunduz, Nangarhar) by pyrethrum spray catch, hand collection methods through WHO/EMRO coordination and from Chabahar County in Iran by pyrethrum spray catch method. Identification was performed using reliable identification key.

RESULTS

Totally, 800 female Anopheles mosquitos, 400 from each country were identified at species level. Anopheles composition in Afghanistan was An. superpictus, An. stephensi and An. hyrcanus. Most prevalent species in Badakhshan and Kunduz were An. superpictus, whereas An. stephensi and An. hyrcanus were respectively found in Nangarhar and Heart. Anopheles species in Chabahar County of Iran were An. stephensi, An. fluviatilis, An. culicifacies and An. sergentii. The most prevalent species was An. stephensi.

CONCLUSION

Current study provides a basis for future research such as detection of Plasmodium infection in collected samples which is on process by the authors, also for effective implementation of evidence-based malaria vector intervention strategies.

Joint spatial time-series epidemiological analysis of malaria and cutaneous leishmaniasis infection

Malaria and leishmaniasis are among the two most important health problems of many developing countries especially in the Middle East and North Africa. It is common for vector-borne infectious diseases to have similar hotspots which may be attributed to the overlapping ecological distribution of the vector. Hotspot analyses were conducted to simultaneously detect the location of local hotspots and test their statistical significance. Spatial scan statistics were used to detect and test hotspots of malaria and cutaneous leishmaniasis (CL) in Afghanistan in 2009. A multivariate negative binomial model was used to simultaneously assess the effects of environmental variables on malaria and CL. In addition to the dependency between malaria and CL disease counts, spatial and temporal information were also incorporated in the model. Results indicated that malaria and CL incidence peaked at the same periods. Two hotspots were detected for malaria and three for CL. The findings in the current study show an association between the incidence of malaria and CL in the studied areas of Afghanistan. The incidence of CL disease in a given month is linked with the incidence of malaria in the previous month. Co-existence of malaria and CL within the same geographical area was supported by this study, highlighting the presence and effects of environmental variables such as temperature and precipitation. People living in areas with malaria are at increased risk for leishmaniasis infection. Local healthcare authorities should consider the co-infection problem by recommending systematic malaria screening for all CL patients.

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.

Seasonal Population Movements and the Surveillance and Control of Infectious Diseases

National policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies.

Forecasting United States heartworm Dirofilaria immitis prevalence in dogs

Background

This paper forecasts next year’s canine heartworm prevalence in the United States from 16 climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described.

Methods

The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 31 million antigen heartworm tests conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on 16 predictive factors, including temperature, precipitation, median household income, local forest and surface water coverage, and presence/absence of eight mosquito species. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year’s regional prevalence.

Results

The correlation between the observed and model-estimated county-by-county heartworm prevalence for the 5-year period 2011–2015 is 0.727, demonstrating reasonable model accuracy. The correlation between 2015 observed and forecasted county-by-county heartworm prevalence is 0.940, demonstrating significant skill and showing that heartworm prevalence can be forecasted reasonably accurately.

Conclusions

The forecast presented herein can a priori alert veterinarians to areas expected to see higher than normal heartworm activity. The proposed methods may prove useful for forecasting other diseases.

Mapping Malaria Risk in Low Transmission Settings: Challenges and Opportunities

As malaria transmission declines, it becomes increasingly focal and prone to outbreaks. Understanding and predicting patterns of transmission risk becomes an important component of an effective elimination campaign, allowing limited resources for control and elimination to be targeted cost-effectively. Malaria risk mapping in low transmission settings is associated with some unique challenges. This article reviews the main challenges and opportunities related to risk mapping in low transmission areas including recent advancements in risk mapping low transmission malaria, relevant metrics, and statistical approaches and risk mapping in post-elimination settings.

Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence

The long-term goal of the global effort to tackle malaria is national and regional elimination and eventually eradication. Fine scale multi-temporal mapping in low malaria transmission settings remains a challenge and the World Health Organisation propose use of surveillance in elimination settings. Here, we show how malaria incidence can be modelled at a fine spatial and temporal resolution from health facility data to help focus surveillance and control to population not attending health facilities. Using Namibia as a case study, we predicted the incidence of malaria, via a Bayesian spatio-temporal model, at a fine spatial resolution from parasitologically confirmed malaria cases and incorporated metrics on healthcare use as well as measures of uncertainty associated with incidence predictions. We then combined the incidence estimates with population maps to estimate clinical burdens and show the benefits of such mapping to identifying areas and seasons that can be targeted for improved surveillance and interventions. Fine spatial resolution maps produced using this approach were then used to target resources to specific local populations, and to specific months of the season. This remote targeting can be especially effective where the population distribution is sparse and further surveillance can be limited to specific local areas.

Geography and social distribution of malaria in Indonesian Papua: a cross-sectional study

Background

Despite being one of the world’s most affected regions, only little is known about the social and spatial distributions of malaria in Indonesian Papua. Existing studies tend to be descriptive in nature; their inferences are prone to confounding and selection biases. At the same time, there remains limited malaria-cartographic activity in the region. Analysing a subset (N = 22,643) of the National Basic Health Research 2007 dataset (N = 987,205), this paper aims to quantify the district-specific risk of malaria in Papua and to understand how socio-demographic/economic factors measured at individual and district levels are associated with individual’s probability of contracting the disease.

Methods

We adopt a Bayesian hierarchical logistic regression model that accommodates not only the nesting of individuals within the island’s 27 administrative units but also the spatial autocorrelation among these locations. Both individual and contextual characteristics are included as predictors in the model; a normal conditional autoregressive prior and an exchangeable one are assigned to the random effects. Robustness is then assessed through sensitivity analyses using alternative hyperpriors.

Results

We find that rural Papuans as well as those who live in poor, densely forested, lowland districts are at a higher risk of infection than their counterparts. We also find age and gender differentials in malaria prevalence, if only to a small degree. Nine districts are estimated to have higher-than-expected malaria risks; the extent of spatial variation on the island remains notable even after accounting for socio-demographic/economic risk factors.

Conclusions

Although we show that malaria is geography-dependent in Indonesian Papua, it is also a disease of poverty. This means that malaria eradication requires not only biological (proximal) interventions but also social (distal) ones.

Spatial and socio-economic effects on malaria morbidity in children under 5 years in Malawi in 2012

BACKGROUND

Malaria is a major health challenge in sub-Saharan Africa with children under 5 being most vulnerable. Identifying regions of greater malarial burden is vital in targeting interventions.

METHODS

This study analysed malaria morbidity using data from the Malawi 2012 Malaria Indicator Survey that were obtained from Demographic and Health Survey (DHS) program website. These data captured malaria related information on children under 5. Poisson regression was done to determine associations between outcome (number of children under 5 with malaria in household) and explanatory variables. A Bayesian smoothing approach was employed to adjust for spatial random effects on child related variables.

RESULTS

There were 1878 households in 140 clusters. The number of children under five was 1900. Spatially structured effects accounted for more than 90% of random effects as these had a mean of 1.32 (95% Credible Interval (CI)=0.37, 2.50) whilst spatially unstructured had a mean of 0.10 (CI=9.0 × 10(-4), 0.38). Spatially adjusted significant variables were; type of place of residence (urban or rural) [posterior odds ratio (POR)=2.06; CI= 1.27, 3.34], not owning land [POR=1.77; CI=1.19, 2.64], not staying in a slum [POR=0.52; CI=0.33, 0.83] and enhanced vegetation index [POR=0.02; CI=0.00, 1.08]. A trend was observed on usage of insecticide treated mosquito nets [POR=0.80; CI=0.63, 1.03].

CONCLUSION

This study showed that malaria is a disease of poverty. Enhanced vegetation index was an important factor in malaria morbidity. The central region was identified as the area with greatest disease burden.

Malaria's contribution to World War One - the unexpected adversary

Malaria in the First World War was an unexpected adversary. In 1914, the scientific community had access to new knowledge on transmission of malaria parasites and their control, but the military were unprepared, and underestimated the nature, magnitude and dispersion of this enemy. In summarizing available information for allied and axis military forces, this review contextualizes the challenge posed by malaria, because although data exist across historical, medical and military documents, descriptions are fragmented, often addressing context specific issues. Military malaria surveillance statistics have, therefore, been summarized for all theatres of the War, where available. These indicated that at least 1.5 million solders were infected, with case fatality ranging from 0.2 -5.0%. As more countries became engaged in the War, the problem grew in size, leading to major epidemics in Macedonia, Palestine, Mesopotamia and Italy. Trans-continental passages of parasites and human reservoirs of infection created ideal circumstances for parasite evolution. Details of these epidemics are reviewed, including major epidemics in England and Italy, which developed following home troop evacuations, and disruption of malaria control activities in Italy. Elsewhere, in sub-Saharan Africa many casualties resulted from high malaria exposure combined with minimal control efforts for soldiers considered semi-immune. Prevention activities eventually started but were initially poorly organized and dependent on local enthusiasm and initiative. Nets had to be designed for field use and were fundamental for personal protection. Multiple prevention approaches adopted in different settings and their relative utility are described. Clinical treatment primarily depended on quinine, although efficacy was poor as relapsing Plasmodium vivax and recrudescent Plasmodium falciparum infections were not distinguished and managed appropriately. Reasons for this are discussed and the clinical trial data summarized, as are controversies that arose from attempts at quinine prophylaxis (quininization). In essence, the First World War was a vast experiment in political, demographic, and medical practice which exposed large gaps in knowledge of tropical medicine and unfortunately, of malaria. Research efforts eventually commenced late in the War to address important clinical questions which established a platform for more effective strategies, but in 1918 this relentless foe had outwitted and weakened both allied and axis powers.