Malaria seasonality and rainfall seasonality in Sri Lanka are correlated in space

Seasonality influences key physiological components contributing to Culex pipiens vector competence

Mosquitoes are the most important animal vector of disease on the planet, transmitting a variety of pathogens of both medical and veterinary importance. Mosquito-borne diseases display distinct seasonal patterns driven by both environmental and biological variables. However, an important, yet unexplored component of these patterns is the potential for seasonal influences on mosquito physiology that may ultimately influence vector competence. To address this question, we selected Culex pipiens, a primary vector of the West Nile virus (WNV) in the temperate United States, to examine the seasonal impacts on mosquito physiology by examining known immune and bacterial components implicated in mosquito arbovirus infection. Semi-field experiments were performed under spring, summer, and late-summer conditions, corresponding to historically low-, medium-, and high-intensity periods of WNV transmission, respectively. Through these experiments, we observed differences in the expression of immune genes and RNA interference (RNAi) pathway components, as well as changes in the distribution and abundance of Wolbachia in the mosquitoes across seasonal cohorts. Together, these findings support the conclusion that seasonal changes significantly influence mosquito physiology and components of the mosquito microbiome, suggesting that seasonality may impact mosquito susceptibility to pathogen infection, which could account for the temporal patterns in mosquito-borne disease transmission.

The relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya: A time-series analysis of monthly incidence data from 2008 to 2019

Background

Despite considerable progress made over the past 20 years in reducing the global burden of malaria, the disease remains a major public health problem and there is concern that climate change might expand suitable areas for transmission. This study investigated the relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya.

Methods

Bayesian negative binomial models were fitted to monthly malaria incidence data, extracted from records of patients with febrile illnesses visiting the Lwak Mission Hospital between 2008 and 2019. Data pertaining to bed net use and socio-economic status (SES) were obtained from household surveys. Climatic proxy variables obtained from remote sensing were included as covariates in the models. Bayesian variable selection was used to determine the elapsing time between climate suitability and malaria incidence.

Results

Malaria incidence increased by 50% from 2008 to 2010, then declined by 73% until 2015. There was a resurgence of cases after 2016, despite high bed net use. Increase in daytime land surface temperature was associated with a decline in malaria incidence (incidence rate ratio [IRR] = 0.70, 95% Bayesian credible interval [BCI]: 0.59-0.82), while rainfall was associated with increased incidence (IRR = 1.27, 95% BCI: 1.10-1.44). Bed net use was associated with a decline in malaria incidence in children aged 6-59 months (IRR = 0.78, 95% BCI: 0.70-0.87) but not in older age groups, whereas SES was not associated with malaria incidence in this population.

Conclusions

Variability in climatic factors showed a stronger effect on malaria incidence than bed net use. Bed net use was, however, associated with a reduction in malaria incidence, especially among children aged 6-59 months after adjusting for climate effects. To sustain the downward trend in malaria incidence, this study recommends continued distribution and use of bed nets and consideration of climate-based malaria early warning systems when planning for future control interventions.

Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019

Background: Environmentally related morbidity and mortality still remain high worldwide, although they have decreased significantly in recent decades. This study aims to forecast malaria epidemics taking into account climatic and spatio-temporal variations and therefore identify geo-climatic factors predictive of malaria prevalence from 2001 to 2019 in the Democratic Republic of Congo. Methods: This is a retrospective longitudinal ecological study. The database of the Directorate of Epidemiological Surveillance including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. The impact of climatic factors on malaria morbidity was modeled using the Generalized Poisson Regression, a predictive model with the dependent variable Y the count of the number of occurrences of malaria cases during a period of time adjusting for risk factors. Results: Our results show that the average prevalence rate of malaria in the last 19 years is 13,246 (1,178,383−1,417,483) cases per 100,000 people at risk. This prevalence increases significantly during the whole study period (p < 0.0001). The year 2002 was the most morbid with 2,913,799 (120,9451−3,830,456) cases per 100,000 persons at risk. Adjusting for other factors, a one-day in rainfall resulted in a 7% statistically significant increase in malaria cases (p < 0.0001). Malaria morbidity was also significantly associated with geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at a two-meter altitude and malaria morbidity (p < 0.0001). Conclusions: In this study, we have established the association between malaria morbidity and geo-climatic predictors such as geographical location, total evaporation under shelter and maximum daily temperature at a two-meter altitude. We show that the average number of malaria cases increased positively as a function of the average number of rainy days, the total quantity of rainfall and the average daily temperature. These findings are important building blocks to help the government of DRC to set up a warning system integrating the monitoring of rainfall and temperature trends and the early detection of anomalies in weather patterns in order to forecast potential large malaria morbidity events.

Assessment of environmental variability on malaria transmission in a malaria-endemic rural dry zone locality of Sri Lanka: The wavelet approach

Malaria is a global public health concern and its dynamic transmission is still a complex process. Malaria transmission largely depends on various factors, including demography, geography, vector dynamics, parasite reservoir, and climate. The dynamic behaviour of malaria transmission has been explained using various statistical and mathematical methods. Of them, wavelet analysis is a powerful mathematical technique used in analysing rapidly changing time-series to understand disease processes in a more holistic way. The current study is aimed at identifying the pattern of malaria transmission and its variability with environmental factors in Kataragama, a malaria-endemic dry zone locality of Sri Lanka, using a wavelet approach. Monthly environmental data including total rainfall and mean water flow of the “Menik Ganga” river; mean temperature, mean minimum and maximum temperatures and mean relative humidity; and malaria cases in the Kataragama Medical Officer of Health (MOH) area were obtained from the Department of Irrigation, Department of Meteorology and Malaria Research Unit (MRU) of University of Colombo, respectively, for the period 1990 to 2005. Wavelet theory was applied to analyze these monthly time series data. There were two significant periodicities in malaria cases during the period of 1992–1995 and 1999–2000. The cross-wavelet power spectrums revealed an anti-phase correlation of malaria cases with mean temperature, minimum temperature, and water flow of “Menik Ganga” river during the period 1991–1995, while the in-phase correlation with rainfall is noticeable only during 1991–1992. Relative humidity was similarly associated with malaria cases between 1991–1992. It appears that environmental variables have contributed to a higher incidence of malaria cases in Kataragama in different time periods between 1990 and 2005.

Malaria Risk Stratification and Modeling the Effect of Rainfall on Malaria Incidence in Eritrea

Background

Malaria risk stratification is essential to differentiate areas with distinct malaria intensity and seasonality patterns. The development of a simple prediction model to forecast malaria incidence by rainfall offers an opportunity for early detection of malaria epidemics.

Objectives

To construct a national malaria stratification map, develop prediction models and forecast monthly malaria incidences based on rainfall data.

Methods

Using monthly malaria incidence data from 2012 to 2016, the district level malaria stratification was constructed by nonhierarchical clustering. Cluster validity was examined by the maximum absolute coordinate change and analysis of variance (ANOVA) with a conservative post hoc test (Bonferroni) as the multiple comparison test. Autocorrelation and cross-correlation analyses were performed to detect the autocorrelation of malaria incidence and the lagged effect of rainfall on malaria incidence. The effect of rainfall on malaria incidence was assessed using seasonal autoregressive integrated moving average (SARIMA) models. Ljung-Box statistics for model diagnosis and stationary R-squared and Normalized Bayesian Information Criteria for model fit were used. Model validity was assessed by analyzing the observed and predicted incidences using the spearman correlation coefficient and paired samples t-test.

Results

A four cluster map (high risk, moderate risk, low risk, and very low risk) was the most valid stratification system for the reported malaria incidence in Eritrea. Monthly incidences were influenced by incidence rates in the previous months. Monthly incidence of malaria in the constructed clusters was associated with 1, 2, 3, and 4 lagged months of rainfall. The constructed models had acceptable accuracy as 73.1%, 46.3%, 53.4%, and 50.7% of the variance in malaria transmission were explained by rainfall in the high-risk, moderate-risk, low-risk, and very low-risk clusters, respectively.

Conclusion

Change in rainfall patterns affect malaria incidence in Eritrea. Using routine malaria case reports and rainfall data, malaria incidences can be forecasted with acceptable accuracy. Further research should consider a village or health facility level modeling of malaria incidence by including other climatic factors like temperature and relative humidity.

An analysis of the influence of the local effects of climatic and hydrological factors affecting new malaria cases in riverine areas along the Rio Negro and surrounding Puraquequara Lake, Amazonas, Brazil

A study was conducted at three sampling regions along the Rio Negro and surrounding Puraquequara Lake, Amazonas, Brazil. The aim was to determine the influence of the local effects of climatic and hydrological variables on new malaria cases. Data was gathered on the river level, precipitation, air temperature, and the number of new cases of autochthonous malaria between January 2003 and December 2013. Monthly averages, time series decompositions, cross-correlations, and multiple regressions revealed different relationships at each location. The sampling region in the upper Rio Negro indicated no statistically significant results. However, monthly averages suggest that precipitation and air temperature correlate positively with the occurrence of new cases of malaria. In the mid Rio Negro and Puraquequara Lake, the river level positively correlated, and temperature negatively correlated with new transmissions, while precipitation correlated negatively in the mid Rio Negro and positively on the lake. Overall, the river level is a key variable affecting the formation of breeding sites, while precipitation may either develop or damage them. A negative temperature correlation is associated with the occurrence of new annual post-peak cases of malaria, when the monthly average exceeds 28.5 °C. This suggests that several factors contribute to the occurrence of new malaria cases as higher temperatures are reached at the same time as precipitation and the river levels are lowest. Differences between signals and correlation lags indicate that local characteristics have an impact on how different variables influence the disease vector's life cycle, pathogens, and consequently, new cases of malaria.

Effect of climatic variability on malaria trends in Baringo County, Kenya

BACKGROUND

Malaria transmission in arid and semi-arid regions of Kenya such as Baringo County, is seasonal and often influenced by climatic factors. Unravelling the relationship between climate variables and malaria transmission dynamics is therefore instrumental in developing effective malaria control strategies. The main aim of this study was to describe the effects of variability of rainfall, maximum temperature and vegetation indices on seasonal trends of malaria in selected health facilities within Baringo County, Kenya.

METHODS

Climate variables sourced from the International Research Institute (IRI)/Lamont-Doherty Earth Observatory (LDEO) climate database and malaria cases reported in 10 health facilities spread across four ecological zones (riverine, lowland, mid-altitude and highland) between 2004 and 2014 were subjected to a time series analysis. A negative binomial regression model with lagged climate variables was used to model long-term monthly malaria cases. The seasonal Mann-Kendall trend test was then used to detect overall monotonic trends in malaria cases.

RESULTS

Malaria cases increased significantly in the highland and midland zones over the study period. Changes in malaria prevalence corresponded to variations in rainfall and maximum temperature. Rainfall at a time lag of 2 months resulted in an increase in malaria transmission across the four zones while an increase in temperature at time lags of 0 and 1 month resulted in an increase in malaria cases in the riverine and highland zones, respectively.

CONCLUSION

Given the existence of a time lag between climatic variables more so rainfall and peak malaria transmission, appropriate control measures can be initiated at the onset of short and after long rains seasons.

Seasonally dependent relationships between indicators of malaria transmission and disease provided by mathematical model simulations

Evaluating the effectiveness of malaria control interventions on the basis of their impact on transmission as well as impact on morbidity and mortality is becoming increasingly important as countries consider pre-elimination and elimination as well as disease control. Data on prevalence and transmission are traditionally obtained through resource-intensive epidemiological and entomological surveys that become difficult as transmission decreases. This work employs mathematical modeling to examine the relationships between malaria indicators allowing more easily measured data, such as routine health systems data on case incidence, to be translated into measures of transmission and other malaria indicators. Simulations of scenarios with different levels of malaria transmission, patterns of seasonality and access to treatment were run with an ensemble of models of malaria epidemiology and within-host dynamics, as part of the OpenMalaria modeling platform. For a given seasonality profile, regression analysis mapped simulation results of malaria indicators, such as annual average entomological inoculation rate, prevalence, incidence of uncomplicated and severe episodes, and mortality, to an expected range of values of any of the other indicators. Results were validated by comparing simulated relationships between indicators with previously published data on these same indicators as observed in malaria endemic areas. These results allow for direct comparisons of malaria transmission intensity estimates made using data collected with different methods on different indicators. They also address key concerns with traditional methods of quantifying transmission in areas of differing transmission intensity and sparse data. Although seasonality of transmission is often ignored in data compilations, the models suggest it can be critically important in determining the relationship between transmission and disease. Application of these models could help public health officials detect changes of disease dynamics in a population and plan and assess the impact of malaria control interventions.

While malaria is still a major public health problem in many parts of the world, control programs have greatly reduced the burden of disease in recent years and many countries are now considering the goal of elimination. Unfortunately, malaria transmission becomes more difficult to measure when it is low because traditional methods involve capturing mosquitoes; an expensive and time-consuming technique. To measure transmission in areas without adequate field data, we run simulations of a mathematical model of malaria over a range of transmission intensities and seasonal patterns to examine how different measurements of malaria (prevalence, clinical disease, and death) relate to each other, how they relate to transmission, and if the relationships are likely to vary by seasonal pattern of transmission. These simulated relationships allow us to translate easily measured data, such as clinical case incidence seen at health facilities, into estimates of transmission. This technique can help public health officials plan and assess the impact of malaria control interventions, even in areas without intensive research activities.

Infection and co-infection with helminths and Plasmodium among school children in Côte d'Ivoire: results from a National Cross-Sectional Survey

BACKGROUND

Helminth infection and malaria remain major causes of ill-health in the tropics and subtropics. There are several shared risk factors (e.g., poverty), and hence, helminth infection and malaria overlap geographically and temporally. However, the extent and consequences of helminth-Plasmodium co-infection at different spatial scales are poorly understood.

METHODOLOGY

This study was conducted in 92 schools across Côte d'Ivoire during the dry season, from November 2011 to February 2012. School children provided blood samples for detection of Plasmodium infection, stool samples for diagnosis of soil-transmitted helminth (STH) and Schistosoma mansoni infections, and urine samples for appraisal of Schistosoma haematobium infection. A questionnaire was administered to obtain demographic, socioeconomic, and behavioral data. Multinomial regression models were utilized to determine risk factors for STH-Plasmodium and Schistosoma-Plasmodium co-infection.

PRINCIPAL FINDINGS

Complete parasitological and questionnaire data were available for 5,104 children aged 5-16 years. 26.2% of the children were infected with any helminth species, whilst the prevalence of Plasmodium infection was 63.3%. STH-Plasmodium co-infection was detected in 13.5% and Schistosoma-Plasmodium in 5.6% of the children. Multinomial regression analysis revealed that boys, children aged 10 years and above, and activities involving close contact to water were significantly and positively associated with STH-Plasmodium co-infection. Boys, wells as source of drinking water, and water contact were significantly and positively associated with Schistosoma-Plasmodium co-infection. Access to latrines, deworming, higher socioeconomic status, and living in urban settings were negatively associated with STH-Plasmodium co-infection; whilst use of deworming drugs and access to modern latrines were negatively associated with Schistosoma-Plasmodium co-infection.

CONCLUSIONS/SIGNIFICANCE

More than 60% of the school children surveyed were infected with Plasmodium across Côte d'Ivoire, and about one out of six had a helminth-Plasmodium co-infection. Our findings provide a rationale to combine control interventions that simultaneously aim at helminthiases and malaria.

Assessing seasonal variations and age patterns in mortality during the first year of life in Tanzania

Lack of birth and death registries in most of developing countries, particularly those in sub-Saharan Africa led to the establishment of Demographic Surveillance Systems (DSS) sites which monitor large population cohorts within defined geographical areas. DSS collects longitudinal data on migration, births, deaths and their causes via verbal autopsies. DSS data provide an opportunity to monitor many health indicators including mortality trends. Mortality rates in Sub-Sahara Africa show seasonal patterns due to high infant and child malaria-related mortality which is influenced by seasonal features present in environmental and climatic factors. However, it is unclear whether seasonal patterns differ by age in the first few months of life. This study provides an overview of approaches to assess, capture and detect seasonality peaks and patterns in mortality using the infant mortality data from the Rufiji DSS, Tanzania. Seasonality was best captured using Bayesian negative binomial models with time and cycle dependent seasonal parameters and autoregressive temporal error terms. Seasonal patterns are similar among different age groups during infancy and timing of their mortality peaks do not differ. Seasonality in mortality rates with two peaks per year is pronounced which corresponds to rainy seasons. Understanding of these trends is important for public health preparedness.

Temporal correlation between malaria and rainfall in Sri Lanka

Background

Rainfall data have potential use for malaria prediction. However, the relationship between rainfall and the number of malaria cases is indirect and complex.

Methods

The statistical relationships between monthly malaria case count data series and monthly mean rainfall series (extracted from interpolated station data) over the period 1972 – 2005 in districts in Sri Lanka was explored in four analyses: cross-correlation; cross-correlation with pre-whitening; inter-annual; and seasonal inter-annual regression.

Results

For most districts, strong positive correlations were found for malaria time series lagging zero to three months behind rainfall, and negative correlations were found for malaria time series lagging four to nine months behind rainfall. However, analysis with pre-whitening showed that most of these correlations were spurious. Only for a few districts, weak positive (at lags zero and one) or weak negative (at lags two to six) correlations were found in pre-whitened series. Inter-annual analysis showed strong negative correlations between malaria and rainfall for a group of districts in the centre-west of the country. Seasonal inter-annual analysis showed that the effect of rainfall on malaria varied according to the season and geography.

Conclusion

Seasonally varying effects of rainfall on malaria case counts may explain weak overall cross-correlations found in pre-whitened series, and should be taken into account in malaria predictive models making use of rainfall as a covariate.