Malaria Incidence Data Research Articles

The short-term impact of a malaria elimination initiative in Southern Mozambique: Application of the synthetic control method to routine surveillance data.

In public health epidemiology, quasi-experimental methods are widely used to estimate the causal impacts of interventions. In this paper, we demonstrate the contribution the synthetic control method (SCM) can make in evaluating public health interventions, when routine surveillance data are available and the validity of other quasi-experimental approaches may be in question. In our application, we evaluate the short-term effects of a large-scale Mass Drug Administration (MDA) based malaria elimination initiative in Southern Mozambique. We apply the SCM to district level weekly malaria incidence data and compare the observed reduction in age group specific malaria incidence. Between August 2015 and April 2017, a total of 13,322 (78%) cases of malaria were averted relative to the synthetic control. During the peak malaria seasons, the elimination initiative resulted in an 87% reduction in Year 1 (December 2015-April 2016), and 79% reduction in Year 2 (December 2016-April 2017). Comparison with an interrupted time series approach shows the SCM accounts for pre-intervention trends in the data and post-intervention weather events influencing malaria cases. We conclude MDA brought about a drastic reduction in malaria burden and can be a useful addition to existing (or new) vector control strategies and tools in accelerating towards elimination.

Forest-going risks

Malaria Forest malaria occurs among economically driven migrants working in the Amazon. However, in the literature, conclusions about the role of forest activities in relation to malaria incidence are conflicting. Rerolle et al. investigated the risk of forest malaria in Lao People's Democratic Republic (PDR) in the context of a nationwide malaria elimination program. High-resolution Landsat remote-sensing imagery of forest cover and malaria incidence data were systematically gathered from four northern and four southern districts of Lao PDR between 2013 and 2016. Deforestation in a 30-kilometer radius around villages was associated with higher malaria incidence within a year, particularly of Plasmodium falciparum malaria, but declined over 3 years. In Lao PDR, unlike the Amazon, it is resident not migrant populations who suffer from forest malaria. eLife 10 , e56974 (2021).

Climate change and the dynamics of age-related malaria incidence in Southern Africa

In the last decade, many malaria-endemic countries, like Zambia, have achieved significant reductions in malaria incidence among children <5 years old but face ongoing challenges in achieving similar progress against malaria in older age groups. In parts of Zambia, changing climatic and environmental factors are among those suspectedly behind high malaria incidence. Changes and variations in these factors potentially interfere with intervention program effectiveness and alter the distribution and incidence patterns of malaria differentially between young children and the rest of the population.We used parametric and non-parametric statistics to model the effects of climatic and socio-demographic variables on age-specific malaria incidence vis-à-vis control interventions. Linear regressions, mixed models, and Mann-Kendall tests were implemented to explore trends, changes in trends, and regress malaria incidence against environmental and intervention variables.Our study shows that while climate parameters affect the whole population, their impacts are felt most by people aged ≥5 years. Climate variables influenced malaria substantially more than mosquito nets and indoor residual spraying interventions. We establish that climate parameters negatively impact malaria control efforts by exacerbating the transmission conditions via more conducive temperature and rainfall environments, which are augmented by cultural and socioeconomic exposure mechanisms. We argue that an intensified communications and education intervention strategy for behavioural change specifically targeted at ≥5 aged population where incidence rates are increasing, is urgently required and call for further malaria stratification among the ≥5 age groups in the routine collection, analysis and reporting of malaria mortality and incidence data.

Malaria Exposure in Ann Township, Myanmar, as a Function of Land Cover and Land Use: Combining Satellite Earth Observations and Field Surveys.

Despite progress toward malaria elimination in the Greater Mekong Subregion, challenges remain owing to the emergence of drug resistance and the persistence of focal transmission reservoirs. Malaria transmission foci in Myanmar are heterogeneous and complex, and many remaining infections are clinically silent, rendering them invisible to routine monitoring. The goal of this research is to define criteria for easy‐to‐implement methodologies, not reliant on routine monitoring, that can increase the efficiency of targeted malaria elimination strategies. Studies have shown relationships between malaria risk and land cover and land use (LCLU), which can be mapped using remote sensing methodologies. Here we aim to explain malaria risk as a function of LCLU for five rural villages in Myanmar's Rakhine State. Malaria prevalence and incidence data were analyzed through logistic regression with a land use survey of ~1,000 participants and a 30‐m land cover map. Malaria prevalence per village ranged from 5% to 20% with the overwhelming majority of cases being subclinical. Villages with high forest cover were associated with increased risk of malaria, even for villagers who did not report visits to forests. Villagers living near croplands experienced decreased malaria risk unless they were directly engaged in farm work. Finally, land cover change (specifically, natural forest loss) appeared to be a substantial contributor to malaria risk in the region, although this was not confirmed through sensitivity analyses. Overall, this study demonstrates that remotely sensed data contextualized with field survey data can be used to inform critical targeting strategies in support of malaria elimination.

Estimating the optimal interval between rounds of indoor residual spraying of insecticide using malaria incidence data from cohort studies.

Indoor residual spraying (IRS) reduces vector densities and malaria transmission, however, the most effective spraying intervals for IRS have not been well established. We estimated the optimal timing interval for IRS using a statistical approach. Six rounds of IRS were implemented in Tororo District, a historically high malaria transmission setting in Uganda, during the study period (3 rounds with bendiocarb active ingredient (Ficam®): December 2014 to December 2015, and 3 rounds with pirimiphos methyl active ingredient (Actellic 300®CS): June 2016 to July 2018). A generalized additive model was used to estimate the optimal timing interval for IRS based on the predicted malaria incidence. The model was fitted to clinical incidence data from a cohort of children aged 0.5-10 years from selected households observed throughout the study period. 494 children, 67% aged less than 5 years at enrolment were analysed. Six-months period incidence of malaria decreased from 2.96 per person-years at the baseline to 1.74 following the first round of IRS and then to 0.02 after 6 rounds of IRS. The optimal time interval for IRS differed between bendiocarb and pirimiphos methyl and by IRS round. To retain an optimum impact, bendiocarb would require respraying 17 (95% CI: 14.2-21.0) weeks after application whereas pirimiphos methyl could remain impactful for 40 (95% CI: 37.0-42.8) weeks, although in the final year this estimates 36 (95% CI: 32.7-37.7) weeks. However, we could not estimate from the data the optimal time after the second and third rounds of bendiocarb and after the second round of pirimiphos methyl. Neither the amount of rainfall nor the EIR nor the distribution of nets were found to be statistically significant for determining the time period between spray rounds. In our setting, the effect of the two IRS products was distinct. Statistically, pirimiphos methyl provided a longer window of protection than bendiocarb, although impact varied between different spray rounds and years which was not explained by rainfall or EIR or distribution of nets in our statistical approach. Understanding the effectiveness of IRS and how long it lasts can help for planning campaigns, but one should consider the financial cost and insecticide resistance. Monitoring the timing of spray campaigns using clinical incidence could be repeated in future programs to help determine the average period of protectivity of these products.

Modelling of malaria incidence in Akure, Nigeria: negative binomial approach

The connections between malaria incidence and climate variability have been studied in recent time using some mathematical and statistical models. Many of the statistical models in literature focused on time series approach based on Box–Jenkins methodology. However, fitting time series model based on the Box–Jenkins methodology may be challenging. Most malaria incidence data are count and are over-dispersed. In this study, negative binomial models were formulated for fitting malaria incidence in Akure—one of the epidemic cities in Nigeria. In particular, negative binomial models were formulated for each of the number of outpatient individuals, number of inpatient individuals and mortality count as a function of some climate variables. It was found that an increase in minimum temperature and relative humidity at lag 1 significantly increased the chance of malaria transmission and thereby leads to an increase in the number of inpatient and outpatient individuals, as well as the total number of malaria cases. The minimum temperature, rainfall amount and relative humidity of the study area have a significant impact on the increase of number of inpatient and outpatient individuals while mortality count depends on the total number of reported malaria cases. The findings from this study is to offer in-depth understanding on climate-malaria incidence linkages in Akure, Nigeria.

Fever From the Lost Forest: Impacts and Management of Health Externalities From Land Use Change in Brazil

Land use change alters the movement of people, pathogens, and vectors, creating novel pathways for disease emergence. A growing body of empirical evidence finds that land use change affects the incidence of vector-borne disease. When management decisions and policy do not consider this relationship, there is potential social welfare loss through health externalities. Herein we develop a dynamic model of optimal land use where both the state and control variable contribute to a negative externality- disease incidence. We compare the net social benefits and path of land conversion under an optimal management regime with scenarios in which the externalities are ignored. Using a two-tiered payment mechanism we demonstrate how the externalities can be internalized by adjusting the payments along the dynamic path of land use. The model is calibrated using malaria incidence and economic data from the Legal Amazon of Brazil. We find that ignoring the health costs of land use change reduces social net benefits of land management and impacts optimal land conversion trajectories. We then simulate the dynamic path of socially optimal management and examine the sensitivity of our predictions by modifying the model assumptions.

Spatio-temporal analysis of association between incidence of malaria and environmental predictors of malaria transmission in Nigeria

Malaria still poses a significant threat in Nigeria despite the various efforts to abate its transmission. Certain environmental factors have been implicated to increase the risk of malaria in Nigeria and other affected countries. The study aimed to evaluate the spatial and temporal association between the incidence of malaria and some environmental risk factors in Nigeria. The study used malaria incidence and environmental risk factors data emanating from 2015 Nigeria Malaria Indicator Survey accessed from the Demographic and Health Survey database. A total of 333 and 326 clusters throughout the country were used for malaria incidence study and environmental variables respectively. The spatial autocorrelation of malaria incidence and hotspot analysis was determined by the Moran’s diagram and local Moran’s I index, respectively. The relationships between the malaria incidence and the ecological predictors of transmission were analysed in all the six geopolitical zones of Nigeria from 2000–2015 using ordinary least square (OLS), spatial lag model (SLM), and spatial error model (SEM). Annual rainfall, precipitation and proximity to water showed significant positive relationship with the incidence rate of malaria in the OLS model (P < 0.01), whereas aridity was negatively related to malaria incidence (P < 0.001) in the same model. The rate of incidence of malaria increased significantly with increase in temperature, aridity, rainfall and proximity to water in the SEM whereas only temperature and proximity to water have significant positive effect on malaria incidence in the SLM. The modelling of the ecological predictors of malaria transmission and spatial maps provided in this study could aid in developing framework to mitigate malaria and identify its hotspots for urgent intervention in the endemic regions.

The Relation Between Climatic Factors and Malaria Incidence in Sistan and Baluchestan, Iran

Malaria is a major health problem in many tropical and subtropical countries and in the south of Iran. In this study, due to the importance of the disease in Sistan and Baluchestan province, the influence of temperature, humidity, and rainfall on malaria has been evaluated in areas with a high incidence of malaria. Malaria incidence data were inquired from the Province Health Authority, and climatic variables were inquired from the Bureau of Meteorology from 2000 to 2012 and were analyzed on a monthly basis. Changes in incidence of malaria with climatic factors were analyzed by negative binomial regression by Stata 11, and the correlations were calculated with Minitab15 for determining the potential impact of meteorological variables with and without lags on malaria transmission. The incidence of malaria had a significant positive correlation with the average, minimum, and maximum monthly temperatures and a negative correlation with rainfall and low humidity (&lt;60%). However, humidity &gt;60% had a positive impact on incidence; as in the town of Chabahar after adjusting variables such as rainfall and temperature; every one percent increase in humidity caused a 4% increase in malaria incidence in the same month and a significant 6% increase in the next month. Temperature and humidity over 60% are effective climate parameters in the incidence of malaria. These factors should be considered in planning for controlling and preventing malaria.

Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000–17: a spatial and temporal modelling study

SummaryBackgroundSince 2000, the scale-up of malaria control interventions has substantially reduced morbidity and mortality caused by the disease globally, fuelling bold aims for disease elimination. In tandem with increased availability of geospatially resolved data, malaria control programmes increasingly use high-resolution maps to characterise spatially heterogeneous patterns of disease risk and thus efficiently target areas of high burden.MethodsWe updated and refined the Plasmodium falciparum parasite rate and clinical incidence models for sub-Saharan Africa, which rely on cross-sectional survey data for parasite rate and intervention coverage. For malaria endemic countries outside of sub-Saharan Africa, we produced estimates of parasite rate and incidence by applying an ecological downscaling approach to malaria incidence data acquired via routine surveillance. Mortality estimates were derived by linking incidence to systematically derived vital registration and verbal autopsy data. Informed by high-resolution covariate surfaces, we estimated P falciparum parasite rate, clinical incidence, and mortality at national, subnational, and 5 × 5 km pixel scales with corresponding uncertainty metrics.FindingsWe present the first global, high-resolution map of P falciparum malaria mortality and the first global prevalence and incidence maps since 2010. These results are combined with those for Plasmodium vivax (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The P falciparum estimates span the period 2000–17, and illustrate the rapid decline in burden between 2005 and 2017, with incidence declining by 27·9% and mortality declining by 42·5%. Despite a growing population in endemic regions, P falciparum cases declined between 2005 and 2017, from 232·3 million (95% uncertainty interval 198·8–277·7) to 193·9 million (156·6–240·2) and deaths declined from 925 800 (596 900–1 341 100) to 618 700 (368 600–952 200). Despite the declines in burden, 90·1% of people within sub-Saharan Africa continue to reside in endemic areas, and this region accounted for 79·4% of cases and 87·6% of deaths in 2017.InterpretationHigh-resolution maps of P falciparum provide a contemporary resource for informing global policy and malaria control planning, programme implementation, and monitoring initiatives. Amid progress in reducing global malaria burden, areas where incidence trends have plateaued or increased in the past 5 years underscore the fragility of hard-won gains against malaria. Efforts towards elimination should be strengthened in such areas, and those where burden remained high throughout the study period.FundingBill & Melinda Gates Foundation.

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.

Serological evidence for a decline in malaria transmission following major scale-up of control efforts in a setting selected for Plasmodium vivax and Plasmodium falciparum malaria elimination in Babile district, Oromia, Ethiopia.

BackgroundFollowing successful malaria control during the last decade, Ethiopia instituted a stepwise malaria elimination strategy in selected low-transmission areas.MethodsCross-sectional surveys were conducted in Babile district, Oromia, Ethiopia from July to November 2017 to evaluate malaria infection status using microscopy and nested polymerase chain reaction (nPCR) and serological markers of exposure targeting Plasmodium falciparum and Plasmodium vivax apical membrane antigen-1 (AMA-1).ResultsParasite prevalence was 1.2% (14/1135) and 5.1% (58/1143) for P. falciparum and 0.4% (5/1135) and 3.6% (41/1143) for P. vivax by microscopy and nPCR, respectively. Antibody prevalence was associated with current infection by nPCR for both P. falciparum (p<0.001) and P. vivax (p=0.014) and showed an age-dependent increase (p<0.001, for both species). Seroconversion curves indicated a decline in malaria exposure 15 y prior to sampling for P. falciparum and 11.5 y prior to sampling for P. vivax, broadly following malaria incidence data from district health offices, with higher antibody titres in adults than children for both species.ConclusionsMalaria transmission declined substantially in the region with continuing heterogeneous but measurable local transmission, arguing in favour of continued and tailored control efforts to accelerate the progress towards elimination efforts.

Fitting Probability Distribution Function to Malaria Incidence Data

Abstract: Malaria remains a major infectious disease that affects millions of people. Once infected with Plasmodium parasites, a host can develop a broad range of clinical presentations, which result from complex interactions between factors derived from the host, the parasite and the environment. Malaria has historically been a very serious health problem and currently poses the most significant threat to the health of Masinde Muliro University of Science and Technology students, data shows that more than 70% percent of pediatric cases are due to malaria.&#x0D; Methodology: Hence, the study aimed to fit malaria incidences dataset for the period 1st January, 2013 to 31st December, 2015. Data on monthly malaria incidence was obtained from the Masinde Muliro University of Science and Technology health service. Gamma, Weibull and Lognormal Distributions were employed to fit the malaria incidence dataset using R-software.&#x0D; Results: High malaria incidences were observed in the months of August, September and November. AIC values results showed that lognormal distribution had the lowest AIC value of 185.9875 followed by the Gamma distribution with a value of 187.8815 and then the Weibull distribution with a value of 188.7271. This confirmed the lognormal distribution to be the best fitting distribution for the malaria incidence dataset&#x0D; Conclusion: The Poisson regression model did not accurately fit the data on malaria incidences due to over dispersion in the data but lognormal distribution was a better fit compared to gamma and Weibull distribution.

Environmental and meteorological factors linked to malaria transmission around large dams at three ecological settings in Ethiopia

BackgroundA growing body of evidence suggests that dams intensify malaria transmission in sub-Saharan Africa. However, the environmental characteristics underpinning patterns in malaria transmission around dams are poorly understood. This study investigated local-scale environmental and meteorological variables linked to malaria transmission around three large dams in Ethiopia.MethodsMonthly malaria incidence data (2010–2014) were collected from health centres around three dams located at lowland, midland and highland elevations in Ethiopia. Environmental (elevation, distance from the reservoir shoreline, Normalized Difference Vegetation Index (NDVI), monthly reservoir water level, monthly changes in water level) and meteorological (precipitation, and minimum and maximum air temperature) data were analysed to determine their relationship with monthly malaria transmission at each dam using correlation and stepwise multiple regression analysis.ResultsVillage distance to reservoir shoreline (lagged by 1 and 2 months) and monthly change in water level (lagged by 1 month) were significantly correlated with malaria incidence at all three dams, while NDVI (lagged by 1 and 2 months) and monthly reservoir water level (lagged by 2 months) were found to have a significant influence at only the lowland and midland dams. Precipitation (lagged by 1 and 2 months) was also significantly associated with malaria incidence, but only at the lowland dam, while minimum and maximum air temperatures (lagged by 1 and 2 months) were important factors at only the highland dam.ConclusionThis study confirmed that reservoir-associated factors (distance from reservoir shoreline, monthly average reservoir water level, monthly water level change) were important predictors of increased malaria incidence in villages around Ethiopian dams in all elevation settings. Reservoir water level management should be considered as an additional malaria vector control tool to help manage malaria transmission around dams.

Predicting the direct and indirect impacts of climate change on malaria in coastal Kenya.

BackgroundThe transmission of malaria is highly variable and depends on a range of climatic and anthropogenic factors. This study investigates the combined, i.e. direct and indirect, impacts of climate change on the dynamics of malaria through modifications in: (i) the sporogonic cycle of Plasmodium induced by air temperature increase, and (ii) the life cycle of Anopheles vector triggered by changes in natural breeding habitat arising from the altered moisture dynamics resulting from acclimation responses of vegetation under climate change. The study is performed for a rural region in Kilifi county, Kenya.Methods and findingsWe use a stochastic lattice-based malaria (SLIM) model to make predictions of changes in Anopheles vector abundance, the life cycle of Plasmodium parasites, and thus malaria transmission under projected climate change in the study region. SLIM incorporates a nonlinear temperature-dependence of malaria parasite development to estimate the extrinsic incubation period of Plasmodium. It is also linked with a spatially distributed eco-hydrologic modeling framework to capture the impacts of climate change on soil moisture dynamics, which served as a key determinant for the formation and persistence of mosquito larval habitats on the land surface. Malaria incidence data collected from 2008 to 2013 is used for SLIM model validation. Projections of climate change and human population for the region are used to run the models for prediction scenarios.Under elevated atmospheric CO2 concentration ([CO2]) only, modeled results reveal wetter soil moisture in the root zone due to the suppression of transpiration from vegetation acclimation, which increases the abundance of Anopheles vectors and the risk of malaria. When air temperature increases are also considered along with elevated [CO2], the life cycle of Anopheles vector and the extrinsic incubation period of Plasmodium parasites are shortened nonlinearly. However, the reduction of soil moisture resulting from higher evapotranspiration due to air temperature increase also reduces the larval habitats of the vector. Our findings show the complicated role of vegetation acclimation under elevated [CO2] on malaria dynamics and indicate an indirect but ignored impact of air temperature increase on malaria transmission through reduction in larval habitats and vector density.ConclusionsVegetation acclimation triggered by elevated [CO2] under climate change increases the risk of malaria. In addition, air temperature increase under climate change has opposing effects on mosquito larval habitats and the life cycles of both Anopheles vectors and Plasmodium parasites. The indirect impacts of temperature change on soil moisture dynamics are significant and should be weighed together with the direct effects of temperature change on the life cycles of mosquitoes and parasites for future malaria prediction and control.

Integrating entomoligical covariates in a predictive model of malaria incidence in Farafangana, Madagascar: Limitations and benefits

Introduction Malaria is one of the leading causes of morbidity in Farafangana, an urban area in the southeastern coast of Madagascar with around 35,000 inhabitants. Predictive models of incidence are needed to strengthen prevention measures in case of an epidemic risk. In a context of large scale vector-control interventions, with changing mosquito density and behaviour, entomological surveillance data could be useful to better forecast malaria incidence. Our primary objective was to build a predictive model of malaria incidence (up to two months in advance) in the primary healthcare center (PHC) of Farafangana, including past incidence (at least two months back), climatic, environmental, vector-control and entomological covariates. Our secondary objective was to quantify how entomological covariates might have improved the model fit. Methods Diagnosed malaria incidence data at the PHC of Farafangana came from the Fever Sentinel Surveillance Network of the Institut Pasteur de Madagascar. Climatic data (temperature and precipitations) and environmental data (Normalized Difference Vegetation Index - NDVI) were extracted from the International Research Institute for Climate and Society (missing values were imputed by exponential smoothing). Vector-control covariates, insecticide treated nets from mass distributions and indoor residual spraying, were binary (1 value means effective). From January 2014 until March 2017, human landing mosquito collections were performed every two months for two consecutive nights (from 6 pm to 6 am), inside and outside five houses spread over one central district, close to the PHC. As entomological data were not measured continuously, we made the hypothesis that they could be repeated until the next capture session. We focused our work on the three most abundant vectors: Anopheles gambiae (n = 209), An. coustani (n = 215) and An. funestus (n = 19). Aggressiveness was calculated for each vector species and capture time [number of bites per human and per evening (from 6 to 10 pm), night (from 10 pm to 2 am) or morning (from 2 to 6 am)]. Exophagy percentage was calculated for each species as the number of captures outside houses on the total number of captures (except for An. funestus due to a small number of captures). These entomological data were transformed on a weekly-basis, resulting in 167 weeks of observations. Lags between incidence and non-entomological covariates were determined by cross-correlation maps for quantitative variables and by univariate analyses for binary variables. Accordingly, lagged covariates were built. Only non-correlated lagged covariates were kept (|Pearson's correlation coefficient| Results Lagged NDVI was excluded because of a correlation with lagged temperature and a negative correlation with incidence. Entomological covariates in the reduced model included the evening aggressiveness of An. gambiae, the night and morning aggressiveness of An. funestus and the exophagy percentage of An. gambiae. The model with entomological covariates (R2 = 0.619 and RMSE = 11.607 malaria cases per week) had a better fit than the model without (R2 = 0.406 and RMSE = 14.654 malaria cases per week). Conclusion Entomological surveillance data may improve the prediction of malaria incidence, even in a context of large scale vector-control and when mosquitos are only captured every two months and from one town district. More frequent captures may generate a better predictive model. However, field Anopheles surveillance remains time-and-money consuming. More studies in various transmission contexts are needed to confirm these results and assess benefits in terms of malaria control and prevention.

Modeling the impact of biolarvicides on malaria transmission

Biolarvicides are in use in several parts of the world for malaria vector control. We propose a five compartment dynamical systems model to study malaria transmission when biolarvicides are administered, to study the impact of this environmentally safe method on malaria spread. A comprehensive analysis of the model is presented. Model analysis shows that the basic reproductive rate R is larger in the absence of biolarvicides as compared to their presence. Theoretical analysis is corroborated by data from field studies. We show that there exist intermediate parameter regimes that separate disease-free and endemic states, which can in turn be modulated by biolarvicide use. Using Latin hypercube sampling we study the sensitivity of the model to parameter value changes. Calibration of our model to mosquito population and biolarvicide data for indoor and outdoors scenarios, yield parameter values hitherto not available or measurable. We validate our model with malaria incidence data from a region in India and provide predictions for malaria incidence in the presence and absence of biolarvicide. This model provides a prognostic tool to field work involving biolarvicide use in control of malaria.

Evaluating Efficacy of Landsat-Derived Environmental Covariates for Predicting Malaria Distribution in Rural Villages of Vhembe District, South Africa.

Malaria in South Africa is still a problem despite existing efforts to eradicate the disease. In the Vhembe District Municipality, malaria prevalence is still high, with a mean incidence rate of 328.2 per 100,0000persons/year. This study aimed at evaluating environmental covariates, such as vegetation moisture and vegetation greenness, associated with malaria vector distribution for better predictability towards rapid and efficient disease management and control. The 2005 malaria incidence data combined with Landsat 5 ETM were used in this study. A total of nine remotely sensed covariates were derived, while pseudo-absences in the ratio of 1:2 (presence/absence) were generated at buffer distances of 0.5-20km from known presence locations. A stepwise logistic regression model was applied to analyse the spatial distribution of malaria in the area. A buffer distance of 10km yielded the highest classification accuracy of 82% at a threshold of 0.9. This model was significant (ρ<0.05) and yielded a deviance (D2) of 36%. The significantly positive relationship (ρ<0.05) between the soil-adjusted vegetation index and malaria distribution at all buffer distances suggests that malaria vector (Anopheles arabiensis) prefer productive and greener vegetation. The significant negative relationship between water/moisture index (a1 index) and malaria distribution in buffer distances of 0.5, 10, and 20km suggest that malaria distribution increases with a decrease in shortwave reflectance signal. The study has shown that suitable habitats of malaria vectors are generally found within a radius of 10km in semi-arid environments, and this insight can be useful to aid efforts aimed at putting in place evidence-based preventative measures against malaria infections. Furthermore, this result is important in understanding malaria dynamics under the current climate and environmental changes. The study has also demonstrated the use of Landsat data and the ability to extract environmental conditions which favour the distribution of malaria vector (An. arabiensis) such as the canopy moisture content in vegetation, which serves as a surrogate for rainfall.

The Relative Contribution of Symptomatic and Asymptomatic Plasmodium vivax and Plasmodium falciparum Infections to the Infectious Reservoir in a Low-Endemic Setting in Ethiopia.

The majority of Plasmodium vivax and Plasmodium falciparum infections in low-endemic settings are asymptomatic. The relative contribution to the infectious reservoir of these infections compared to clinical malaria cases is currently unknown. We assessed infectivity of passively recruited symptomatic malaria patients (n = 41) and community-recruited asymptomatic individuals with microscopy-detected (n = 41) and polymerase chain reaction (PCR)-detected infections (n = 82) using membrane feeding assays with Anopheles arabiensis mosquitoes in Adama, Ethiopia. Malaria incidence and prevalence data were used to estimate the contributions of these populations to the infectious reservoir. Overall, 34.9% (29/83) of P. vivax- and 15.1% (8/53) P. falciparum-infected individuals infected ≥1 mosquitoes. Mosquito infection rates were strongly correlated with asexual parasite density for P. vivax (ρ = 0.63; P < .001) but not for P. falciparum (ρ = 0.06; P = .770). Plasmodium vivax symptomatic infections were more infectious to mosquitoes (infecting 46.5% of mosquitoes, 307/660) compared to asymptomatic microscopy-detected (infecting 12.0% of mosquitoes, 80/667; P = .005) and PCR-detected infections (infecting 0.8% of mosquitoes, 6/744; P < .001). Adjusting for population prevalence, symptomatic, asymptomatic microscopy-detected, and PCR-detected infections were responsible for 8.0%, 76.2%, and 15.8% of the infectious reservoir for P. vivax, respectively. For P. falciparum, mosquito infections were sparser and also predominantly from asymptomatic infections. In this low-endemic setting aiming for malaria elimination, asymptomatic infections were highly prevalent and responsible for the majority of onward mosquito infections. The early identification and treatment of asymptomatic infections might accelerate elimination efforts.

Evaluation of environmentally driven models for early warning of malaria: an exploratory study

BackgroundRemotely-sensed earth observation data have the potential to inform early warning systems for malaria and other environmentally mediated diseases due to the lagged relation between environmental conditions at present and disease transmission potential in the near future. A common approach is to use total rainfall and mean temperature at a specific time lag to forecast malaria incidence, without justification for the choice of predictor variables or the length of the time lag. Here we explore a number of commonly used formulations of models to forecast malaria incidences across multiple districts using a variety of environmental indices derived from near-real-time satellite remote-sensing data and summarized using polynomial distributed lags. MethodsWe used weekly malaria incidence data from 2012 to 2017 in 47 districts in the Amhara Region of Ethiopia, a region of unstable malaria transmission where epidemics are driven by environmental conditions, combined with district-level rainfall, land surface temperature, and vegetation greenness, and vegetation water content summarised at lags of 1–16 weeks. We fit models containing various combinations of parameters related to trend, seasonality, and transformations of the environmental indices. We assessed model accuracy using time series cross validation. FindingsModels containing a linear effect of trend, Fourier components for seasonality, and climatological anomalies of rainfall, daytime land surface temperature, and normalised difference water index (NDWI) produced the most useful forecasts. Optimal distributed lag functions varied geographically, but generally showed the most positive correlations with malaria incidence at lags of 4–6 weeks. InterpretationTime series models used for early warning of malaria outbreaks are improved by incorporating other environmental indices in addition to rainfall and temperature, by using distributed lags for summarising environmental effects, and by using Fourier components for seasonality combined with environmental anomalies rather than raw environmental indices. FundingNational Institute of Allergy and Infectious Diseases (grant number R01AI079411).