Seasonal associations of climatic drivers and malaria in the highlands of Ethiopia

Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology-Based Model

A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub-Saharan Africa. Irrigation has been associated with increased malaria risk, but risk prediction remains difficult due to the heterogeneity of irrigation and the environment. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats. By coupling a hydrologic model with an agent-based malaria model for a sugarcane plantation site in Arjo, Ethiopia, we demonstrated how incorporating hydrologic processes to estimate larval habitats can affect malaria transmission. Using the coupled model, we then examined the impact of an existing irrigation scheme on malaria transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitat area by around two-fold and resulted in reduction in malaria transmission by 60%. In addition, irrigation increased all habitat types in the dry season by up to 7.4 times. It converted temporary and semi-permanent habitats to permanent habitats during the rainy season, which grew by about 24%. Consequently, malaria transmission was sustained all-year round and intensified during the main transmission season, with the peak shifted forward by around 1 month. Lastly, we evaluated the spatiotemporal distribution of adult vectors under the effect of irrigation by resolving habitat heterogeneity. These findings could help larval source management by identifying transmission hotspots and prioritizing resources for malaria elimination planning.

Space-time modelling of monthly malaria incidence for seasonal associated drivers and early epidemic detection in Southern Ethiopia

BACKGROUND

Although Ethiopia has made great strides in recent years to reduce the threat of malaria, the disease remains a significant issue in most districts of the country. It constantly disappears in parts of the areas before reappearing in others with erratic transmission rates. Thus, developing a malaria epidemic early warning system is important to support the prevention and control of the incidence.

METHODS

Space-time malaria risk mapping is essential to monitor and evaluate priority zones, refocus intervention, and enable planning for future health targets. From August 2013 to May 2019, the researcher considered an aggregated count of genus Plasmodium falciparum from 149 districts in Southern Ethiopia. Afterwards, a malaria epidemic early warning system was developed using model-based geostatistics, which helped to chart the disease's spread and future management.

RESULTS

Risk factors like precipitation, temperature, humidity, and nighttime light are significantly associated with malaria with different rates across the districts. Districts in the southwest, including Selamago, Bero, and Hamer, had higher rates of malaria risk, whereas in the south and centre like Arbaminch and Hawassa had moderate rates. The distribution is inconsistent and varies across time and space with the seasons.

CONCLUSION

Despite the importance of spatial correlation in disease risk mapping, it may occasionally be a good idea to generate epidemic early warning independently in each district to get a quick picture of disease risk. A system like this is essential for spotting numerous inconsistencies in lower administrative levels early enough to take corrective action before outbreaks arise.

One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level

BACKGROUND

Primary healthcare systems require adequate staffing to meet the needs of their local population. Guidelines typically use population ratio targets for healthcare workers, such as Ethiopia's goal of two health extension workers for every five thousand people. However, fixed ratios do not reflect local demographics, fertility rates, disease burden (e.g., malaria endemicity), or trends in these values. Recognizing this, we set out to estimate the clinical workload to meet the primary healthcare needs in Ethiopia by region.

METHODS

We utilize the open-source R package PACE-HRH for our analysis, which is a stochastic Monte Carlo simulation model that estimates workload for a specified service package and population. Assumptions and data inputs for region-specific fertility, mortality, disease burden were drawn from literature, DHS, and WorldPop. We project workload until 2035 for seven regions and two charted cities of Ethiopia.

RESULTS

All regions and charted cities are expected to experience increased workload between 2021 and 2035 for a starting catchment of five thousand people. The expected (mean) annual clinical workload varied from 2,930 h (Addis) to 3,752 h (Gambela) and increased by 19-28% over fifteen years. This results from a decline in per capita workload (due to declines in fertility and infectious diseases), overpowered by total population growth. Pregnancy, non-communicable diseases, sick child care, and nutrition remain the largest service categories, but their priority shifts substantially in some regions by 2035. Sensitivity analysis shows that fertility assumptions have major implications for workload. We incorporate seasonality and estimate monthly variation of up to 8.9% (Somali), though most services with high variability are declining.

CONCLUSIONS

Regional variation in demographics, fertility, seasonality, and disease trends all affect the workload estimates. This results in differences in expected clinical workload, the level of uncertainty in those estimates, and relative priorities between service categories. By showing these differences, we demonstrate the inadequacy of a fixed population ratio for staffing allocation. Policy-makers and regulators need to consider these factors in designing their healthcare systems, or they risk sub-optimally allocating workforce and creating inequitable access to care.

A Retrospective Analysis of Malaria Trends in Leka Dulecha Health Center over the Last Ten Years (2013-2022), Western Oromia, East Wollega Zone

Background

Malaria is a serious public health concern in the world, and it causes a major socioeconomic problem in Ethiopia. Malaria data trend analysis of health facilities is useful to understand the prevalence and incidence of malaria cases and implementing evidence-based malaria control strategies. Hence, the main objective of this study was to investigate the malaria trends over the last ten years (2013-2022) at Leka Dulecha Health Center, East Wollega Zone, Western Oromia. Methodology. A retrospective study was conducted at Leka Dulecha Health Center to determine the trends of malaria prevalence by considering the malaria registration laboratory logbook for the last ten years from 2013 to 2022. Hence, to do this, sociodemographic data, years, months, and malaria prevalence were collected using a predesigned data collection sheet recorded from perspective between years.

Results

In the last ten years, a total of 30,576.00 suspected malaria cases were examined at Leka Dulecha Health Center, and out of these, 7,413.00 (24.24%) confirmed malaria cases were reported. In this health center, malaria cases were reported among both sexes and all age categories, but male (3,951.00, 54%) and age groups ≥ 15 years (3,994, 54%) were the most affected. The highest peak of malaria cases was reported during the autumn season (September, October, and November) followed by the spring season (March, April, and May) in the years of 2013 and 2007. In this study, the prevalence of malaria species was identified as Plasmodium falciparum, Plasmodium vivax, and mixed cases, with 5,014 (68%), 1,123 (15%), and 1,848 (25%), while Plasmodium falciparum was reported as the highest recorded cases.

Conclusion

Males and above 15 years old were more affected than the others. The highest peak malaria prevalence appeared from September to December of 2017 and 2013 years. Therefore, proper planning, implementation, and monitor of malaria prevention and control activities should be strengthened at all levels.

Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda

As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.

Seasonal and spatial variations of malaria transmissions in northwest Ethiopia: Evaluating climate and environmental effects using generalized additive model

The impacts of climate change and environmental predictors on malaria epidemiology remain unclear and not well investigated in the Sub-Sahara African region. This study was aimed to investigate the nonlinear effects of climate and environmental factors on monthly malaria cases in northwest Ethiopia, considering space-time interaction effects. The monthly malaria cases and populations sizes of the 152 districts were obtained from the Amhara public health institute and the central statistical agency of Ethiopia. The climate and environmental data were retrieved from US National Oceanic and Atmospheric Administration. The data were analyzed using a spatiotemporal generalized additive model. The spatial, temporal, and space-time interaction effects had higher contributions in explaining the spatiotemporal distribution of malaria transmissions. Malaria transmission was seasonal, in which a higher number of cases occurred from September to November. The long-term trend of malaria incidence has decreased between 2012 and 2018 and has turned to an increased pattern since 2019. Areas neighborhood to the Abay gorge and Benshangul-Gumuz, South Sudan, and Sudan border have higher spatial effects. Climate and environmental predictors had significant nonlinear effects, in which their effects are not stationary through the ranges of values of variables, and they had a smaller contributions in explaining the variabilities of malaria incidence compared to seasonal, spatial and temporal effects. Effects of climate and environmental predictors were nonlinear and varied across areas, ecology, and landscape of the study sites, which had little contribution to explaining malaria transmission variabilities with an account of space and time dimensions. Hence, exploring and developing an early warning system that predicts the outbreak of malaria transmission would have an essential role in controlling, preventing, and eliminating malaria in areas with lower and higher transmission levels and ultimately lead to the achievement of malaria GTS milestones.

Cattle feeding tendency of Anopheles mosquitoes and their infection rates in Aradum village, North Wollo, Ethiopia: an implication for animal-based malaria control strategies

BACKGROUND

Surveillance of indoor and outdoor resting malaria vector populations is crucial to monitor possible changes in vector resting and feeding behaviours. This study was conducted to assess the resting behaviour, blood meal sources and circumsporozoite (CSP) rates of Anopheles mosquito in Aradum village, Northern Ethiopia.

METHODS

Mosquito collection was conducted from September 2019 to February 2020 using clay pots (indoor and outdoor), pit shelter and pyrethrum spray catches (PSC). The species of Anopheles gambiae complex and Anopheles funestus group were identified using polymerase chain reaction (PCR). Enzyme-linked immunosorbent assay (ELISA) was done to determine CSP and blood meal sources of malaria vectors.

RESULTS

A total of 775 female Anopheles mosquitoes were collected using the clay pot, PSC and pit shelter. Seven Anopheles mosquito species were identified morphologically, of which Anopheles demeilloni (593; 76.5%) was the dominant species followed by An. funestus group (73; 9.4%). Seventy-three An. funestus group screened by PCR, 91.8% (67/73) were identified as Anopheles leesoni and only 2.7% (2/73) were found to be Anopheles parensis. The molecular speciation of 71 An. gambiae complex confirmed 91.5% (65/71) of Anopheles arabiensis. The majority of Anopheles mosquitoes were collected from outdoor pit shelter (42.2%) followed by outdoor clay pots. The majority of the blood meal of An. demeilloni (57.5%; 161/280), An. funestus sensu lato 10 (43.5%) and An. gambiae (33.3%; 14/42) originated from bovine. None of the 364 Anopheles mosquitoes tested for Plasmodium falciparum and Plasmodium vivax sporozoite infections were positive.

CONCLUSION

Since the Anopheles mosquitoes in the area prefer to bite cattle, it may be best to target them with an animal-based intervention. Clay pots could be an alternative tool for outdoor monitoring of malaria vectors in areas where pit shelter construction is not possible.

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

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

Treatment outcome and factors associated with mortality due to malaria in Munini District Hospital, Rwanda in 2016-2017: Retrospective cross-sectional study

Introduction

Malaria is a major public health burden in developing countries despite efforts made by several countries. This disease leads to high morbidity and mortality among Rwandans, particularly in the Southern Province where it was the sixth national cause of morality; at Munini hospital it is the first cause of mortality, but the associated factors remain unknown. In this study, we determined the factors associated with deaths among patients with severe malaria to come up with evidence-based interventions to prevent malaria and its factors.

Methods

A retrospective cross-sectional study was conducted on malaria patients who were treated at the Munini District Hospital from 2016 to 2017. Data were collected from the hospital records or registers relating to patients who were admitted with severe malaria. The odds ratio was estimated by bivariate logistic regression and multivariate hierarchical regression models for determining the associated factors of deaths. Data were analyzed using STATA/MP Version 14.1 and Epi-info with proportions.

Results

The study population were mostly women (n = 237, 59.1%), farmers (n = 313, 78.05%), aged 16-30 years (n = 107, 26.68%). Our results indicated that the majority of deaths were women (56.25%). Socio-economic and clinical determinants are important predictors of death among patients with severe malaria. Patients with coma had higher odds of dying (AOR = 7.31, 95% CI :3.33-16.1, p < 0.001) than those who were not. The possibility of mortality increased by almost four times in patients who delayed consultation by a day (AOR = 3.7, 95%CI:1.8-4.1; p < 0.001) compared to those who came in very early. Patients who had severe malaria in the dry season were at a lower risk of mortality (AOR = 0.23, 95%CI:0.08-0.64, p = 0.005) compared to those with severe malaria during the rainy season.

Conclusion

Lack of health insurance, age of the patient, delayed diagnosis, coma, proximity and access to healthcare services, and weather conditions were the major factors associated with mortality among patients with severe malaria. Comprehensive, long-term, equity-based healthcare interventions and immediate care strategies are recommended.

Climate Change and Cascading Risks from Infectious Disease

Climate change is adversely affecting the burden of infectious disease throughout the world, which is a health security threat. Climate-sensitive infectious disease includes vector-borne diseases such as malaria, whose transmission potential is expected to increase because of enhanced climatic suitability for the mosquito vector in Asia, sub-Saharan Africa, and South America. Climatic suitability for the mosquitoes that can carry dengue, Zika, and chikungunya is also likely to increase, facilitating further increases in the geographic range and longer transmission seasons, and raising concern for expansion of these diseases into temperate zones, particularly under higher greenhouse gas emission scenarios. Early spring temperatures in 2018 seem to have contributed to the early onset and extensive West Nile virus outbreak in Europe, a pathogen expected to expand further beyond its current distribution, due to a warming climate. As for tick-borne diseases, climate change is projected to continue to contribute to the spread of Lyme disease and tick-borne encephalitis, particularly in North America and Europe. Schistosomiasis is a water-borne disease and public health concern in Africa, Latin America, the Middle East, and Southeast Asia; climate change is anticipated to change its distribution, with both expansions and contractions expected. Other water-borne diseases that cause diarrheal diseases have declined significantly over the last decades owing to socioeconomic development and public health measures but changes in climate can reverse some of these positive developments. Weather and climate events, population movement, land use changes, urbanization, global trade, and other drivers can catalyze a succession of secondary events that can lead to a range of health impacts, including infectious disease outbreaks. These cascading risk pathways of causally connected events can result in large-scale outbreaks and affect society at large. We review climatic and other cascading drivers of infectious disease with projections under different climate change scenarios. Supplementary file1 (MP4 328467 KB).

Cloud-based applications for accessing satellite Earth observations to support malaria early warning

Malaria epidemics can be triggered by fluctuations in temperature and precipitation that influence vector mosquitoes and the malaria parasite. Identifying and monitoring environmental risk factors can thus provide early warning of future outbreaks. Satellite Earth observations provide relevant measurements, but obtaining these data requires substantial expertise, computational resources, and internet bandwidth. To support malaria forecasting in Ethiopia, we developed software for Retrieving Environmental Analytics for Climate and Health (REACH). REACH is a cloud-based application for accessing data on land surface temperature, spectral indices, and precipitation using the Google Earth Engine (GEE) platform. REACH can be implemented using the GEE code editor and JavaScript API, as a standalone web app, or as package with the Python API. Users provide a date range and data for 852 districts in Ethiopia are automatically summarized and downloaded as tables. REACH was successfully used in Ethiopia to support a pilot malaria early warning project in the Amhara region. The software can be extended to new locations and modified to access other environmental datasets through GEE.

Spatio-Temporal Variation of Malaria Incidence and Risk Factors in West Gojjam Zone, Northwest Ethiopia

Introduction

Malaria is a life-threatening acute febrile illness which is affecting the lives of millions globally. Its distribution is characterized by spatial, temporal, and spatiotemporal heterogeneity. Detection of the space-time distribution and mapping high-risk areas is useful to target hot spots for effective intervention.

Methods

Time series cross sectional study was conducted using weekly malaria surveillance data obtained from Amhara Public Health Institute. Poisson model was fitted to determine the purely spatial, temporal, and space-time clusters using SaTScan™ 9.6 software. Spearman correlation, bivariate, and multivariable negative binomial regressions were used to analyze the relation of the climatic factors to count of malaria incidence.

Result

Jabitenan, Quarit, Sekela, Bure, and Wonberma were high rate spatial cluster of malaria incidence hierarchically. Spatiotemporal clusters were detected. A temporal scan statistic identified 1 risk period from 1 July 2013 to 30 June 2015. The adjusted incidence rate ratio showed that monthly average temperature and monthly average rainfall were independent predictors for malaria incidence at all lag-months. Monthly average relative humidity was significant at 2 months lag.

Conclusion

Malaria incidence had spatial, temporal, spatiotemporal variability in West Gojjam zone. Mean monthly temperature and rainfall were directly and negatively associated to count of malaria incidence respectively. Considering these space-time variations and risk factors (temperature and rainfall) would be useful for the prevention and control and ultimately achieve elimination.

The changing malaria trend and control efforts in Oromia Special zone, Amhara Regional State, North-East Ethiopia

Background

Countries in malaria endemic regions are determinedly making an effort to achieve the global malaria elimination goals. In Ethiopia, too, all concerned bodies have given attention to this mission as one of their priority areas so that malaria would be eradicated from the country. Despite the success stories from some areas in the country, however, malaria is still a major public health concern in most parts of Ethiopia. Therefore, this study is aimed at analysing the changing malaria trend and assessing the impact of malaria control efforts in one of the malaria endemic regions of Ethiopia.

Methods

Five years data on clinical malaria cases diagnosed and treated at all health facilities (including 28 Health Centres, 105 Health Posts and 2 Hospitals) in Oromia Special zone, Amhara Regional State, Ethiopia, were reviewed for the period from June 2014 to June 2019. Data on different interventional activities undertaken in the zone during the specified period were obtained from the Regional Health Bureau.

Results

The cumulative malaria positivity rate documented in the zone was 12.5% (n = 65,463/524,722). Plasmodium falciparum infection was the dominant malaria aetiology and accounted for 78.9% (n = 51,679). The age group with the highest malaria burden was found to be those aged above 15 years (54.14%, n = 35,443/65,463). The malaria trend showed a sharp decreasing pattern from 19.33% (in 2015) to 5.65% (in 2018), although insignificant increment was recorded in 2019 (8.53%). Distribution of long-lasting insecticidal nets (LLIN) and indoor residual spraying (IRS) were undertaken in the zone once a year only for two years, specifically in 2014 and 2017. In 2014, a single LLIN was distributed per head of households, which was not sufficient for a family size of more than one family member. Number of houses sprayed with indoor residual spray in 2014 and 2017 were 33,314 and 32,184 houses, respectively, leading to the assumption that, 151,444 (25.9%) and 141,641 (24.2%) population were protected in year 2014 and 2017, respectively. The analysis has shown that P. falciparum positivity rate was significantly decreased following the interventional activities by 3.3% (p = 0.009), but interventional efforts did not appear to have significant effect on vivax malaria, as positivity rate of this parasite increased by 1.49% (p = 0.0218).

Conclusion

Malaria burden has shown a decreasing pattern in the study area, although the pattern was not consistent throughout all the years and across the districts in the study area. Therefore, unremitting surveillance along implementation of interventional efforts should be considered taking into account the unique features of Plasmodium species, population dynamics in the zone, seasonality, and malaria history at different districts of the zone should be in place to achieve the envisaged national malaria elimination goal by 2030.

Parous rate and longevity of anophelines mosquitoes in bure district, northwestern Ethiopia

The intensity of malaria transmission is measured by parous rate, daily survival rate, human blood meal frequency, sporozoite rate, and entomological inoculation rates. Female parous status is a key index of vector competence, adult vector longevity, recruitment rate of adult, and the length of a gonotrophic cycle. Hence, the present study was aimed to investigate the parous rate and the longevity of Anopheles mosquitoes in Bure District, Northwestern Ethiopia. Parous rate was estimated as the number of mosquitoes with parous ovaries divided by the number of females dissected multiplied by 100. Mosquito life expectancy (longevity as d) was estimated by. One way- ANOVA was applied to confirm the presence of parous rate difference in the villages (p < 0.05). A total of 952 unfed hosts-seeking Anopheles mosquitoes was dissected for parous rate determination. The overall parous rate of An. arabiensis in the district was 52.0%, and the highest parous rate was recorded in Shnebekuma than other villages (F 2, 33 = 6.974; p = 0.003). Similarly, the parous rate of An. cinereus showed significant variation among villages (F 2, 33 = 5.044, p = 0.012) and the highest rate (63.0%) was recorded in Bukta. The mean longevity of An. funestus, An. arabiensis, An. coustani, An. squamosus, An. pharoensis, and An. cinereus was 6.5 days, 4.6 days, 3.5 days, 3.7 days, 2.7 days, and 2.2 days, respectively. The longevity of each species was not sufficient to complete the life cycle of malaria parasite for malaria transmission throughout the year because P. falciparum requires from 12–14 day.

Detection of temporal, spatial and spatiotemporal clustering of malaria incidence in northwest Ethiopia, 2012–2020

Malaria is one of Ethiopia's most targeted communicable diseases for elimination. Malaria transmission varies significantly across space and time; and Ethiopia had space–time disparity in its transmission intensities. Considering heterogeneity and transmission intensity at the district level could play a crucial role in malaria prevention and elimination. This study aimed to explore temporal, spatial, and spatiotemporal clusters of malaria incidence in northwest Ethiopia. The analysis is based on monthly malaria surveillance data of districts and collected from the Amhara public health institute. The Kulldorff's retrospective space–time scan statistics using a discrete Poisson model were used to detect temporal, spatial, and space–time clusters of malaria incidence with and without adjusting the altitude + LLIN arm. Monthly malaria incidence had seasonal variations, and higher seasonal indices occurred in October and November. The temporal cluster occurred in the higher transmission season between September and December annually. The higher malaria incidence risk occurred between July 2012 and December 2013 (LLR = 414,013.41, RR = 2.54, P < 0.05). The purely spatial clustering result revealed that the most likely cluster occurred in the north and northwest parts of the region while secondary clusters varied in years. The space–time clusters were detected with and without considering altitude + LLIN arm. The most likely space–time cluster was concentrated in northwestern and western parts of the region with a high-risk period between July 2012 and December 2013 (LLR = 880,088.3, RR = 5.5, P < 0.001). We found eight significant space–time clusters using the altitude + LLIN arm. The most likely space–time cluster occurred in the western and northwestern parts of the region in July 2012–December 2013 (LLR = 886,097.7, RR = 5.55, P < 0.05). However, secondary clusters were located in eastern, northwestern, western parts of regions, which had different cases and relative risks in each cluster. Malaria transmission had temporal, spatial, and space–time variation in the region at the district level. Hence, considering these variations and factors contributing to malaria stratification would play an indispensable role in preventing and controlling practices that ultimately leads to malaria eliminations.

Risk factors for some tropical diseases in an African country

BACKGROUND

Often, non-clinical risk factors could affect the predisposition of an individual to diseases. Understanding these factors and their impacts helps in disease prevention and control. This study identified risk factors for malaria, yellow fever, typhoid, chickenpox, measles, hepatitis B, and urinary tract infection in a population in an African country.

METHODS

Our study was an observational, correlational, and quantitative one that explored relationships among risk variables and disease prevalence - without modifying or controlling the variables. Data for this study was obtained through random sampling of a population of patients and physicians in the eastern/southern, western, and northern parts of Nigeria in 2015-2016. A total of 2199 patient consultation forms were returned by 102 (out of 125) physicians, and considered useful for analysis. Demographic data of patients, physicians, and diagnosis outcomes were analysed descriptively through frequency distributions, aggregate analysis, and graphs. The influence of risk factors on the disease manifestations (diagnosis outcomes) was determined using regression analysis.

RESULTS

Our results show that living in a tropical climate is by far a major risk factor associated with tropical diseases (malaria: t = 19.9, typhoid: t = - 3.2, chickenpox: t = - 6.5 and typhoid: t = 12.7). The risk for contracting infections is relative to specific diseases; for example, contact with chickenpox infected person poses a high risk of contracting the virus (t = 41.8), while poor personal hygiene predisposes people to high risk of urinary tract infection (t = 23.6). On the other hand, urbanization and homelessness pose very low risks of disposing the individual to the diseases under consideration, while low fluid intake, lack of voiding, and wearing non-cotton underwear predispose individuals to few diseases.

CONCLUSION

The risk factors identified in our study exert differential and discriminating influences in the causation, predisposition, and transmission of these disease studied. It is recommended that significant effort be devoted by governments in the tropics to the mitigation of these modifiable risk factors. The most important strategy to mitigate the occurrence of these risk factors will be improving the living conditions of people and the provision of social protection measures to reduce the occurrence and burden of these diseases.

Inequities in childhood anaemia at provincial borders in Mozambique: cross-sectional study results from multilevel Bayesian analysis of 2018 National Malaria Indicator Survey

Objectives

This study aims to identify the child-level, maternal-level, household-level and community-level determinants of anaemia among children aged 6–59 months, and determine the inequities of anaemia prevalence across communities in Mozambique.

Design

Cross-sectional study.

Setting

Mozambique.

Participants

This study used data of a weighted population of 3946 children, 6–59 months, delivered by women between 15 and 49 years of age, from the 2018 Mozambique Malaria Indicator Survey.

Primary outcome measure

Child’s anaemic status, measured as altitude-adjusted haemoglobin concentration (in g/L); the severity of anaemia was categorised based on predefined threshold values. Multilevel Bayesian linear regressions identified key determinants of childhood anaemia. Based on data availability and policy implications, spatial analysis was used to determine geographical variation of anaemia at the community level and areas with higher risks.

Results

The mean prevalence of childhood anaemia was 77.7% (SD: 5.5%). Provincially, Cabo Delgado province (86.2%) had the highest prevalence, Maputo province (70.2%) the lowest. Children with excess risk were mostly found in communities that had proximity to provincial borders: Niassa-Cabo Delgado-Nampula triprovincial border, Gaza-Inhambane border, Zambezia-Nampula border and provinces of Manica and Inhambane. Children with anaemia tended to be younger, males and at risk of having malaria because they were not sleeping under mosquito nets. In addition, children from poor families relative to children from wealthier households and those living in female-headed households were prone to anaemia.

Conclusion

Findings from this study provide evidence that spatial inequities in childhood anaemia exist in Mozambique, mostly concentrated in the communities living close to the provincial borders. Anaemia among children could be effectively reduced through malaria prevention, for example, bed netting. Interventions are needed that generate income for households, increase community support for households headed by women, improve malaria control, build capacity of healthcare workers to manage severely anaemic children and health education for mothers.

Malaria risk factors in northern Namibia: The importance of occupation, age and mobility in characterizing high-risk populations

In areas of low and unstable transmission, malaria cases occur in populations with lower access to malaria services and interventions, and in groups with specific malaria risk exposures often away from the household. In support of the Namibian National Vector Borne Disease Program's drive to better target interventions based upon risk, we implemented a health facility-based case control study aimed to identify risk factors for symptomatic malaria in Zambezi Region, northern Namibia. A total of 770 febrile individuals reporting to 6 health facilities and testing positive by rapid diagnostic test (RDT) between February 2015 and April 2016 were recruited as cases; 641 febrile individuals testing negative by RDT at the same health facilities through June 2016 were recruited as controls. Data on socio-demographics, housing construction, overnight travel, use of malaria prevention and outdoor behaviors at night were collected through interview and recorded on a tablet-based questionnaire. Remotely-sensed environmental data were extracted for geo-located village residence locations. Multivariable logistic regression was conducted to identify risk factors and latent class analyses (LCA) used to identify and characterize high-risk subgroups. The majority of participants (87% of cases and 69% of controls) were recruited during the 2016 transmission season, an outbreak year in Southern Africa. After adjustment, cases were more likely to be cattle herders (Adjusted Odds Ratio (aOR): 4.46 95%CI 1.05-18.96), members of the police or other security personnel (aOR: 4.60 95%CI: 1.16-18.16), and pensioners/unemployed persons (aOR: 2.25 95%CI 1.24-4.08), compared to agricultural workers (most common category). Children (aOR 2.28 95%CI 1.13-4.59) and self-identified students were at higher risk of malaria (aOR: 4.32 95%CI 2.31-8.10). Other actionable risk factors for malaria included housing and behavioral characteristics, including traditional home construction and sleeping in an open structure (versus modern structure: aOR: 2.01 95%CI 1.45-2.79 and aOR: 4.76 95%CI: 2.14-10.57); cross border travel in the prior 30 days (aOR: 10.55 95%CI 2.94-37.84); and outdoor agricultural work at night (aOR: 2.09 95%CI 1.12-3.87). Malaria preventive activities were all protective and included personal use of an insecticide treated net (ITN) (aOR: 0.61 95%CI 0.42-0.87), adequate household ITN coverage (aOR: 0.63 95%CI 0.42-0.94), and household indoor residual spraying (IRS) in the past year (versus never sprayed: (aOR: 0.63 95%CI 0.44-0.90). A number of environmental factors were associated with increased risk of malaria, including lower temperatures, higher rainfall and increased vegetation for the 30 days prior to diagnosis and residing more than 5 minutes from a health facility. LCA identified six classes of cases, with class membership strongly correlated with occupation, age and select behavioral risk factors. Use of ITNs and IRS coverage was similarly low across classes. For malaria elimination these high-risk groups will need targeted and tailored intervention strategies, for example, by implementing alternative delivery methods of interventions through schools and worksites, as well as the use of specific interventions that address outdoor transmission.

Remote sensing of environmental risk factors for malaria in different geographic contexts

BACKGROUND

Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies.

METHODS

We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models.

RESULTS

We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria.

CONCLUSION

We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.

Comparing malaria early detection methods in a declining transmission setting in northwestern Ethiopia

Background

Despite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world’s population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods.

Methods

We used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three methods for early detection of the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington methods.

Results

All of the methods evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80–100% CDC; 57–100% weekly statistical) and low to moderate alarm specificities (25–40% CDC; 16–61% weekly statistical). Farrington variants had a wide range of scores (20–100% sensitivities; 16–100% specificities) and could achieve various balances between sensitivity and specificity.

Conclusions

Of the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (> 70% sensitivity and > 70% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10850-5.

Bloodmeal sources and feeding behavior of anopheline mosquitoes in Bure district, northwestern Ethiopia

BACKGROUND

Mosquito bloodmeal sources determine the feeding rates, adult survival, fecundity, hatching rates, and developmental times. Only the female Anopheles mosquito takes bloodmeals from humans, birds, mammals, and other vertebrates for egg development. Studies of the host preference patterns in blood-feeding anopheline mosquitoes are crucial to determine malaria vectors. However, the human blood index, foraging ratio, and host preference index of anopheline mosquitoes are not known so far in Bure district, Ethiopia.

METHODS

The origins of bloodmeals from all freshly fed and a few half-gravid exophagic and endophagic females collected using Centers for Disease Control and Prevention light traps were identified as human and bovine using enzyme-linked immunosorbent assay. The human blood index, forage ratio, and host feeding index were calculated.

RESULTS

A total of 617 specimens belonging to An. arabiensis (n = 209), An. funestus (n = 217), An. coustani (n = 123), An. squamosus (n = 54), and An. cinereus (n = 14) were only analyzed using blood ELISA. Five hundred seventy-five of the specimens were positive for blood antigens of the host bloods. All anopheline mosquitoes assayed for a bloodmeal source had mixed- rather than single-source bloodmeals. The FR for humans was slightly > 1.0 compared to bovines for all Anopheles species. HFI for each pair of vertebrate hosts revealed that humans were the slightly preferred bloodmeal source compared to bovines for all species (except An. squamosus), but there was no marked host selection.

CONCLUSIONS

All anopheline mosquitoes assayed for bloodmeal ELISA had mixed feeds, which tends to diminish the density of gametocytes in the mosquito stomach, thereby reducing the chance of fertilization of the female gamete and reducing the chances of a malaria vector becoming infected. Moreover, An. coustani was the only species that had only human bloodmeals, meaning that this species has the potential to transmit the disease. Therefore, combination zooprophylaxis should be reinforced as a means of vector control because the study sites are mixed dwellings.

Alternative Invasion Mechanisms and Host Immune Response to Plasmodium vivax Malaria: Trends and Future Directions

Plasmodium vivax malaria is a neglected tropical disease, despite being more geographically widespread than any other form of malaria. The documentation of P. vivax infections in different parts of Africa where Duffy-negative individuals are predominant suggested that there are alternative pathways for P. vivax to invade human erythrocytes. Duffy-negative individuals may be just as fit as Duffy-positive individuals and are no longer resistant to P.vivax malaria. In this review, we describe the complexity of P. vivax malaria, characterize pathogenesis and candidate invasion genes of P. vivax, and host immune responses to P. vivax infections. We provide a comprehensive review on parasite ligands in several Plasmodium species that further justify candidate genes in P. vivax. We also summarize previous genomic and transcriptomic studies related to the identification of ligand and receptor proteins in P. vivax erythrocyte invasion. Finally, we identify topics that remain unclear and propose future studies that will greatly contribute to our knowledge of P. vivax.

Species diversity and distribution of Anopheles mosquitoes in Bure district, Northwestern Ethiopia

Malaria is one the leading health problem of the Ethiopia. Previously, areas above 2,000 m elevation were considered as malaria free areas. However, the major malaria epidemics were seen in areas at an altitude up to 3,000 m above sea level. These epidemics were due to climate and land-use changes (ecological changes) and still malaria is a growing health problem in highland parts of Ethiopia. This study aimed to investigate the species diversity, abundance and distribution of Anopheles mosquitoes in highland fringe of Bure district, Northwestern Ethiopia. It was done in the three different agroecological villages, Bukta (Irrigated), Workimdr (non-irrigated with few dry season breeding habitats) and Shnebekuma (non-irrigated with many dry season breeding habitats). Anopheles mosquitoes were collected by the Centers for Disease Control and Prevention Light Traps Catches, Pyrethrum Spray Catches, and Artificial Pit Shelters (APSs) from twenty-seven houses, thirty houses, and six APSs, respectively. Anopheles mosquitoes were identified morphologically to species using standard keys. Furthermore, molecular identification of Anopheles gambiae s.l was carried out using species-specific Polymerase Chain Reaction. Independent T-Test and One-way- ANOVA were employed to compare the mean mosquito's density between villages and species, indoor and outdoor host seeking mosquitoes. Descriptive statistic was used to calculate the proportion of each Anopheles species. Nine Anopheles mosquito species were identified in the study area which includes: Anopheles demeilloni, An. arabiensis, An. funestus group, An. coustani, An. squamosus, An. cinereus, An. pharoensis, An. rupicolus, and An. natalensis. Of the 4,703 Anopheles mosquitoes collected, An. demeilloni was the most prominent (50.7%, n = 2383) whereas An. rupicolus (0.03%, n = 3), and An. natalensis (0.02%, n = 1) were the least abundant. Higher mean density of Anopheles mosquitoes was collected from the non-irrigated village (2.395 ± 0.100) than irrigated (1.351 ± 0.109) (p = 0.001). In conclusion, three of the most important malaria vectors (An. arabiensis, An. funestus group and An. pharoensis) of Ethiopia were recorded in the study sites, especially the first two was found thought-out the year. Most of the Anopheles mosquitoes were collected from non-irrigated villages. Thus, breeding habitat management must be practiced throughout the year together with long-lasting insecticide-treated nets and insecticide residual sprays.

Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012-2017) from 1400 persons who sought treatment at Dangassa's community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

Pre-Erythrocytic Vaccines against Malaria

Malaria, caused by the protozoan Plasmodium, is a devastating disease with over 200 million new cases reported globally every year. Although immunization is arguably the best strategy to eliminate malaria, despite decades of research in this area we do not have an effective, clinically approved antimalarial vaccine. The current impetus in the field is to develop vaccines directed at the pre-erythrocytic developmental stages of Plasmodium, utilizing novel vaccination platforms. We here review the most promising pre-erythrocytic stage antimalarial vaccine candidates.

Spatio-Temporal Dynamic of Malaria Incidence: A Comparison of Two Ecological Zones in Mali

Malaria transmission largely depends on environmental, climatic, and hydrological conditions. In Mali, malaria epidemiological patterns are nested within three ecological zones. This study aimed at assessing the relationship between those conditions and the incidence of malaria in Dangassa and Koila, Mali. Malaria data was collected through passive case detection at community health facilities of each study site from June 2015 to January 2017. Climate and environmental data were obtained over the same time period from the Goddard Earth Sciences (Giovanni) platform and hydrological data from Mali hydraulic services. A generalized additive model was used to determine the lagged time between each principal component analysis derived component and the incidence of malaria cases, and also used to analyze the relationship between malaria and the lagged components in a multivariate approach. Malaria transmission patterns were bimodal at both sites, but peak and lull periods were longer lasting for Koila study site. Temperatures were associated with malaria incidence in both sites. In Dangassa, the wind speed (p = 0.005) and river heights (p = 0.010) contributed to increasing malaria incidence, in contrast to Koila, where it was humidity (p < 0.001) and vegetation (p = 0.004). The relationships between environmental factors and malaria incidence differed between the two settings, implying different malaria dynamics and adjustments in the conception and plan of interventions.

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations Over the Amhara Region, Ethiopia

Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from satellite remote sensing or spatial interpolation of ground-based observations in relation to data from 22 meteorological stations in Amhara Region, Ethiopia, for 2003-2016. Famine Early Warning Systems Network (FEWS-Net) Land Data Assimilation System (FLDAS) interpolated temperature showed the lowest bias (mean error (ME) ≈1-3 °C), and error (mean absolute error (MAE) ≈1-3 °C), and the highest correlation with day-to-day variability of station temperature (COR ≈0.7-0.8). In contrast, temperature retrievals from the blended Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave and Moderate-resolution Imaging Spectroradiometer (MODIS) land-surface temperature data had higher bias and error. Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) rainfall showed the least bias and error (ME ≈-0.2-0.2 mm, MAE ≈0.5-2 mm), and the best agreement (COR ≈0.8), with station rainfall data. In contrast FLDAS had the higher bias and error and the lowest agreement and Global Precipitation Mission/Tropical Rainfall Measurement Mission (GPM/TRMM) data were intermediate. This information can inform the selection of geospatial data products for use in climate and disease research and applications.

A genetic algorithm for identifying spatially-varying environmental drivers in a malaria time series model

Time series models of malaria cases can be applied to forecast epidemics and support proactive interventions. Mosquito life history and parasite development are sensitive to environmental factors such as temperature and precipitation, and these variables are often used as predictors in malaria models. However, malaria-environment relationships can vary with ecological and social context. We used a genetic algorithm to optimize a spatiotemporal malaria model by aggregating locations into clusters with similar environmental sensitivities. We tested the algorithm in the Amhara Region of Ethiopia using seven years of weekly Plasmodium falciparum data from 47 districts and remotely-sensed land surface temperature, precipitation, and spectral indices as predictors. The best model identified six clusters, and the districts in each cluster had distinctive responses to the environmental predictors. We conclude that spatial stratification can improve the fit of environmentally-driven disease models, and genetic algorithms provide a practical and effective approach for identifying these clusters.

Spatial modeling, prediction and seasonal variation of malaria in northwest Ethiopia

Objectives

The aim of this study was to determine the spatial modeling, seasonal variation of malaria and making prediction map of malaria in northwest Ethiopia.

Results

The overall average cumulative annual malaria incidence during the study period was 30 per 100 populations at risk. The highest proportion (29.2%) was observed from June 2015 to October 2016. In temporal analysis of clusters, the epidemic was observed from 2015/7/1 to 2016/12/31 throughout the study period in all districts. Hotspot areas with high clusters (p < 0.001) were observed in Metema district it accounts 18.6% of the total malaria cases. An area of high median predicted incidence proportion (> 50%) was seen in the southwest part of the region. Most of the northern part of the study area was predicted to have a low median incidence proportion (< 10%).

Electronic supplementary material

The online version of this article (10.1186/s13104-019-4305-1) contains supplementary material, which is available to authorized users.

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.

Impact of Weekly Climatic Variables on Weekly Malaria Incidence throughout Thailand: A Country-Based Six-Year Retrospective Study

Purpose. This study aimed to evaluate climatic data, including mean temperature, relative humidity, and rainfall, and their association with malaria incidence throughout Thailand from 2012 to 2017. The correlation of climatic parameters including temperature, relative humidity, and rainfall in each province and the weekly malaria incidence was analyzed using Spearman's rank correlation. The results showed that the mean temperature correlated with malaria incidence (p value < 0.05) in 44 provinces in Thailand. These correlations were frequently found in the western and southern parts of Thailand. Relative humidity correlated with malaria incidence (p value < 0.05) in 35 provinces. These correlations were frequently found in the northern and northeastern parts of Thailand. Rainfall correlated with malaria incidence (p value < 0.05) in 38 provinces. These correlations were frequently found in the northern parts and some western parts of Thailand. The impacts of the mean temperature, relative humidity, and rainfall were observed frequently in specific provinces, including Chiang Mai, Chiang Rai, Trat, Kanchanaburi, Ubonratchathani, and Si Sa Ket. This is the first study to report areas where climatic data are associated with malaria incidence throughout Thailand from 2012 to 2017. These results can map out the climatic change process over time and across the country, which is the foundation for effective early warning systems for malaria, public health awareness campaigns, and the adoption of proper adaption measures that will help in malaria detection, diagnosis, and treatment.

Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

Malaria transmission trends and its lagged association with climatic factors in the highlands of Plateau State, Nigeria

Background:

Malaria is a serious disease and still remains a public health problem in many parts of Nigeria.

Objectives:

The aim of this study was to describe malaria transmission trends and analyzed the impact of climatic factors on malaria transmission in the highlands of Plateau State, Central Nigeria.

Methods:

The study was a retrospective survey which used archival data of climate parameters and medical case records on malaria. Rainfall, relative humidity, and temperature data were obtained from the nearest weather stations to the study locations from 1980 to 2015. Data on reported malaria cases were collected from general hospitals in the selected local government areas (LGAs) from 2003 to 2015. Generalized Additive Models were used to model trends in malaria incidences over time, and it is lagged association with climatic factors.

Results:

The results show a significant cyclical trend in malaria incidence in all the study areas (P < 0.001). The association between monthly malaria cases and mean monthly temperature, rainfall, and relative humidity show significant association at different time lags and locations.

Conclusion:

Our findings suggest that climatic factors are among the major determinants of malaria transmission in the highlands of Plateau state except in Jos-North LGA where the low model deviance explained (35.4%) could mean that there are other important factors driving malaria transmission in the area other than climatic factors.

Spatio-temporal modelling of weekly malaria incidence in children under 5 for early epidemic detection in Mozambique

Malaria is a major cause of morbidity and mortality in Mozambique. We present a malaria early warning system (MEWS) for Mozambique informed by seven years of weekly case reports of malaria in children under 5 years of age from 142 districts. A spatio-temporal model was developed based on explanatory climatic variables to map exceedance probabilities, defined as the predictive probability that the relative risk of malaria incidence in a given district for a particular week will exceed a predefined threshold. Unlike most spatially discrete models, our approach accounts for the geographical extent of each district in the derivation of the spatial covariance structure to allow for changes in administrative boundaries over time. The MEWS can thus be used to predict areas that may experience increases in malaria transmission beyond expected levels, early enough so that prevention and response measures can be implemented prior to the onset of outbreaks. The framework we present is also applicable to other climate-sensitive diseases.

Spatial modelling of malaria cases associated with environmental factors in South Sumatra, Indonesia

Background

Malaria, a parasitic infection, is a life-threatening disease in South Sumatra Province, Indonesia. This study aimed to investigate the spatial association between malaria occurrence and environmental risk factors.

Methods

The number of confirmed malaria cases was analysed for the year 2013 from the routine reporting of the Provincial Health Office of South Sumatra. The cases were spread over 436 out of 1613 villages. Six potential ecological predictors of malaria cases were analysed in the different regions using ordinary least square (OLS) and geographically weighted regression (GWR). The global pattern and spatial variability of associations between malaria cases and the selected potential ecological predictors was explored.

Results

The importance of different environmental and geographic parameters for malaria was shown at global and village-level in South Sumatra, Indonesia. The independent variables altitude, distance from forest, and rainfall in global OLS were significantly associated with malaria cases. However, as shown by GWR model and in line with recent reviews, the relationship between malaria and environmental factors in South Sumatra strongly varied spatially in different regions.

Conclusions

A more in-depth understanding of local ecological factors influencing malaria disease as shown in present study may not only be useful for developing sustainable regional malaria control programmes, but can also benefit malaria elimination efforts at village level.

Development of an empirical model to predict malaria outbreaks based on monthly case reports and climate variables in Hefei, China, 1990-2011

Malaria remains a significant public health concern in developing countries. Drivers of malaria transmission vary across different geographical regions. Climatic variables are major risk factor in seasonal and secular patterns of P. vivax malaria transmission along Anhui province. The study aims to forecast malaria outbreaks using empirical model developed in Hefei, China. Data on the monthly numbers of notified malaria cases and climatic factors were obtained for the period of January 1st 1990 to December 31st 2011 from the Hefei CDC and Anhui Institute of Meteorological Sciences, respectively. Two logistic regression models with time series seasonal decomposition were used to explore the impact of climatic and seasonal factors on malaria outbreaks. Sensitivity and specificity statistics were used for evaluating the predictive power. The results showed that relative humidity (OR = 1.171, 95% CI = 1.090-1.257), sunshine (OR = 1.076, 95% CI = 1.043-1.110) and barometric pressure (OR = 1.051, 95% CI = 1.003-1.100) were significantly associated with malaria outbreaks after adjustment for seasonality in Hefei area. The validation analyses indicated the overall agreement of 70.42% (sensitivity: 70.52%; specificity: 70.30%). The research suggested that the empirical model developed based on disease surveillance and climatic conditions may have applications in malaria control and prevention activities.

Transmission dynamics of co-endemic Plasmodium vivax and P. falciparum in Ethiopia and prevalence of antimalarial resistant genotypes

Ethiopia is one of the few African countries where Plasmodium vivax is co-endemic with P. falciparum. Malaria transmission is seasonal and transmission intensity varies mainly by landscape and climate. Although the recent emergence of drug resistant parasites presents a major issue to malaria control in Ethiopia, little is known about the transmission pathways of parasite species and prevalence of resistant markers. This study used microsatellites to determine population diversity and gene flow patterns of P. falciparum (N = 226) and P. vivax (N = 205), as well as prevalence of drug resistant markers to infer the impact of gene flow and existing malaria treatment regimes. Plasmodium falciparum indicated a higher rate of polyclonal infections than P. vivax. Both species revealed moderate genetic diversity and similar population structure. Populations in the northern highlands were closely related to the eastern Rift Valley, but slightly distinct from the southern basin area. Gene flow via human migrations between the northern and eastern populations were frequent and mostly bidirectional. Landscape genetic analyses indicated that environmental heterogeneity and geographical distance did not constrain parasite gene flow. This may partly explain similar patterns of resistant marker prevalence. In P. falciparum, a high prevalence of mutant alleles was detected in codons related to chloroquine (pfcrt and pfmdr1) and sulfadoxine-pyrimethamine (pfdhps and pfdhfr) resistance. Over 60% of the samples showed pfmdr1 duplications. Nevertheless, no mutation was detected in pfK13 that relates to artemisinin resistance. In P. vivax, while sequences of pvcrt-o were highly conserved and less than 5% of the samples showed pvmdr duplications, over 50% of the samples had pvmdr1 976F mutation. It remains to be tested if this mutation relates to chloroquine resistance. Monitoring the extent of malaria spread and markers of drug resistance is imperative to inform policy for evidence-based antimalarial choice and interventions. To effectively reduce malaria burden in Ethiopia, control efforts should focus on seasonal migrant populations.

Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination

Background

Swaziland aims to eliminate malaria by 2020. However, imported cases from neighbouring endemic countries continue to sustain local parasite reservoirs and initiate transmission. As certain weather and climatic conditions may trigger or intensify malaria outbreaks, identification of areas prone to these conditions may aid decision-makers in deploying targeted malaria interventions more effectively.

Methods

Malaria case-surveillance data for Swaziland were provided by Swaziland’s National Malaria Control Programme. Climate data were derived from local weather stations and remote sensing images. Climate parameters and malaria cases between 2001 and 2015 were then analysed using seasonal autoregressive integrated moving average models and distributed lag non-linear models (DLNM).

Results

The incidence of malaria in Swaziland increased between 2005 and 2010, especially in the Lubombo and Hhohho regions. A time-series analysis indicated that warmer temperatures and higher precipitation in the Lubombo and Hhohho administrative regions are conducive to malaria transmission. DLNM showed that the risk of malaria increased in Lubombo when the maximum temperature was above 30 °C or monthly precipitation was above 5 in. In Hhohho, the minimum temperature remaining above 15 °C or precipitation being greater than 10 in. might be associated with malaria transmission.

Conclusions

This study provides a preliminary assessment of the impact of short-term climate variations on malaria transmission in Swaziland. The geographic separation of imported and locally acquired malaria, as well as population behaviour, highlight the varying modes of transmission, part of which may be relevant to climate conditions. Thus, the impact of changing climate conditions should be noted as Swaziland moves toward malaria elimination.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-017-1874-0) contains supplementary material, which is available to authorized users.

Malaria and severe anemia over eight years at Gambo Rural Hospital, southern Ethiopia

Evolution of incident malaria and frequency of anemia were analyzed over eight years in a rural hospital in southern Ethiopia. Capillary blood samples were tested for hemoglobin concentration, and in some instances for malaria parasites, at Gambo Rural General Hospital between January 2007 and September 2014, and the results recorded. Main demographic data were also recorded in subjects with Plasmodium sp. infections. Of a total of 54,493 blood samples taken from 45,096 different patients, 21,723 (39.9%) samples from 19,173 (42.5%) patients were tested for malaria parasites. Malaria was diagnosed in 825 (3.79%, 95% CI 3.55%, 4.06%) instances (58.3% P. vivax and 41.7% P. falciparum; one episode in 575 patients and two episodes in 125 patients). A sustained decrease in yearly incidence of malaria was observed between 2011 (6.1%) and 2014 (2.4%) (p < 0.01). Of all the malaria patients, those with hemoglobin levels less than 8 g/dL, were younger compared to those with levels of 8 g/dL or more (median age of 5 years vs. 18 years; p < 0.01) and more commonly infected with P. falciparum (57.1% vs. 34.8%; p < 0.001). In multivariate analysis, severe anemia (hemoglobin <8 g/dL) in the context of anemia was associated with P falciparum infection (adjusted odd ratio [OR] 2.48, 95% confidence interval [CI] 1.68, 3.65) and younger age (OR 1.06, 95% CI 1.04, 1.07).

Assessing the Risk Factors Associated with Malaria in the Highlands of Ethiopia: What Do We Need to Know?

Malaria has been Ethiopia's predominant communicable disease for decades. Following the catastrophic malaria outbreak in 2003⁻2004, the Federal Ministry of Health (FMoH) took drastic public health actions to lower the burden of malaria. The FMoH achieved significant declines in malaria mortality and incidence, and recently declared its objective to achieve malaria elimination in low malaria transmission areas of Ethiopia by 2020. However, while the overall malaria prevalence has decreased, unpredictable outbreaks increasingly occur irregularly in regions previously considered "malaria-free". Such outbreaks have disastrous consequences on populations of these regions as they have no immunity against malaria. The Amhara Region accounts for 31% of Ethiopia's malaria burden and is targeted for malaria elimination by the FMoH. Amhara's epidemiological surveillance system faces many challenges to detect in a timely manner the unpredictable and irregular malaria outbreaks that occur in areas of otherwise low transmission. Despite the evidence of a shift in malaria transmission patterns, Amhara's malaria control interventions remain constrained to areas that are historically known to have stable malaria transmission. This paper discusses the influence of temperature and precipitation variability, entomological parameters, and human population mobility on malaria transmission patterns across the Amhara Region, and in particular, in areas of unstable transmission. We argue that malaria epidemiological surveillance systems can be improved by accounting for population movements in addition to environmental and entomological factors. However, to date, no study has statistically analyzed the interplay of population dynamics on environmental and entomological drivers of malaria transmission.

Integrating malaria surveillance with climate data for outbreak detection and forecasting: the EPIDEMIA system

Background

Early indication of an emerging malaria epidemic can provide an opportunity for proactive interventions. Challenges to the identification of nascent malaria epidemics include obtaining recent epidemiological surveillance data, spatially and temporally harmonizing this information with timely data on environmental precursors, applying models for early detection and early warning, and communicating results to public health officials. Automated web-based informatics systems can provide a solution to these problems, but their implementation in real-world settings has been limited.

Methods

The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system was designed and implemented to integrate disease surveillance with environmental monitoring in support of operational malaria forecasting in the Amhara region of Ethiopia. A co-design workshop was held with computer scientists, epidemiological modelers, and public health partners to develop an initial list of system requirements. Subsequent updates to the system were based on feedback obtained from system evaluation workshops and assessments conducted by a steering committee of users in the public health sector.

Results

The system integrated epidemiological data uploaded weekly by the Amhara Regional Health Bureau with remotely-sensed environmental data freely available from online archives. Environmental data were acquired and processed automatically by the EASTWeb software program. Additional software was developed to implement a public health interface for data upload and download, harmonize the epidemiological and environmental data into a unified database, automatically update time series forecasting models, and generate formatted reports. Reporting features included district-level control charts and maps summarizing epidemiological indicators of emerging malaria outbreaks, environmental risk factors, and forecasts of future malaria risk.

Conclusions

Successful implementation and use of EPIDEMIA is an important step forward in the use of epidemiological and environmental informatics systems for malaria surveillance. Developing software to automate the workflow steps while remaining robust to continual changes in the input data streams was a key technical challenge. Continual stakeholder involvement throughout design, implementation, and operation has created a strong enabling environment that will facilitate the ongoing development, application, and testing of the system.

Malaria risk in young male travellers but local transmission persists: a case-control study in low transmission Namibia

Background

A key component of malaria elimination campaigns is the identification and targeting of high risk populations. To characterize high risk populations in north central Namibia, a prospective health facility-based case–control study was conducted from December 2012–July 2014. Cases (n = 107) were all patients presenting to any of the 46 health clinics located in the study districts with a confirmed Plasmodium infection by multi-species rapid diagnostic test (RDT). Population controls (n = 679) for each district were RDT negative individuals residing within a household that was randomly selected from a census listing using a two-stage sampling procedure. Demographic, travel, socio-economic, behavioural, climate and vegetation data were also collected. Spatial patterns of malaria risk were analysed. Multivariate logistic regression was used to identify risk factors for malaria.

Results

Malaria risk was observed to cluster along the border with Angola, and travel patterns among cases were comparatively restricted to northern Namibia and Angola. Travel to Angola was associated with excessive risk of malaria in males (OR 43.58 95% CI 2.12–896), but there was no corresponding risk associated with travel by females. This is the first study to reveal that gender can modify the effect of travel on risk of malaria. Amongst non-travellers, male gender was also associated with a higher risk of malaria compared with females (OR 1.95 95% CI 1.25–3.04). Other strong risk factors were sleeping away from the household the previous night, lower socioeconomic status, living in an area with moderate vegetation around their house, experiencing moderate rainfall in the month prior to diagnosis and living <15 km from the Angolan border.

Conclusions

These findings highlight the critical need to target malaria interventions to young male travellers, who have a disproportionate risk of malaria in northern Namibia, to coordinate cross-border regional malaria prevention initiatives and to scale up coverage of prevention measures such as indoor residual spraying and long-lasting insecticide nets in high risk areas if malaria elimination is to be realized.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-017-1719-x) contains supplementary material, which is available to authorized users.

Effect of Deforestation and Land Use Changes on Mosquito Productivity and Development in Western Kenya Highlands: Implication for Malaria Risk

Background

African highlands were known to be free of malaria for the past 50 years. However, the ever growing human population in the highlands of Africa has led to the deforestation and land coverage changes to create space for more land for cultivation, grazing, and house construction materials needs. This has lead to the creation of suitable breeding habitats, which are in open places. Decrease of canopy and forest cover has led to increased temperature both in outdoors and indoors in deforested areas. This increased temperature has resulted in the shortening of developmental stages of aquatic stages of mosquitoes and sporogony development in adult mosquitoes.

Method

Assessment of the effects of deforestation and land coverage changes (decrease), which leads to temperature changes and subsequently increases survivorship of adults and sporogony development in adult mosquitoes’ body was gathered from previous data collected from 2003 to 2012 using different analysis techniques. Habitats productivity, species dynamics and abundance, mosquitoes feeding rates, and sporogony development are presented in relation to temperature changes.

Results

The effects of temperature rise due to land cover changes in highlands of western Kenya on larval developmental rates, adult sporogony developments, and malaria risk in human population were derived. Vector species dynamics and abundance in relation to land use changes have been found to change with time.

Conclusion

This study found that, land cover changes is a key driver for the temperature rise in African highlands and increases the rate of malaria vectors Anopheles gambiae ssp., An. Funestus, and An. arabiensis colonizing the highlands. It has also significantly enhanced sporogony development rate and adult vector survival and therefore the risk of malaria transmission in the highlands.

The effects of electricity network development besides routine malaria control measures in an underdeveloped region in the pre-elimination phase

Background

The main purpose of this study was to investigate the effects of electricity network development on malaria transmission. The study was performed in the rural areas of three districts in Sistan-va-Baluchestan Province, Iran.

Methods

From the mentioned districts, 122 rural communities were selected. The data of the years 2005–2009 were collected retrospectively from data banks of the district health centres and the offices of the local electricity network. Fixed and random effects panel data regression models were fitted to determine the effects of electrification and other variables on malaria transmission during the elimination phase.

Results

It seems that access to electricity of rural communities, if not harmful, has no obvious effect on malaria control and prevention at least during the elimination phase in an underdeveloped region. Elevation above sea level and precipitation during spring and summer were found to be the other important, respectively, time-invariant and time-dependent variables associated with decreasing and increasing malaria transmission. Indoor residual spraying and the use of insecticide-treated mosquito nets were not found to be effective in decreasing malaria transmission in the elimination phase.

Conclusions

The introduction of electricity to a rural community does not guarantee an absolutely good effect on the reduction of malaria transmission.

Seasonal and Geographic Variation of Pediatric Malaria in Burundi: 2011 to 2012

We analyzed hospitalization records from 2011 to 2012 to examine the spatial patterns of pediatric malaria in Burundi. Malaria case data for those below the age of five years were categorized according to the four principal seasons of Burundi, which are two rainy seasons (February to May; September to November) and two dry seasons (June to August; December to January). The Getis-Ord Gi* statistic was used to examine seasonal spatial patterns of pediatric malaria, whereas geographically weighted regression (GWR) were used to examine the potential role of environmental variables on the spatial patterns of cases. There were a total of 19,890 pediatric malaria cases reported during the study period. The incidence among males was higher than that among females; and it was higher in rural districts. The seasonal incidence peaks occurred in the northern half of the country during the wet season while during the dry season, incidence was higher in southern Burundi. Elevation played a greater role in explaining variance in the prevalence of pediatric malaria during seasonal peaks than rainfall. The counterintuitive finding in northern Burundi confirms previous findings and suggests other factors (e.g., land cover/land use) facilitate the persistence of the mosquito population in the highlands of Africa.

Clinical Malaria Transmission Trends and Its Association with Climatic Variables in Tubu Village, Botswana: A Retrospective Analysis

Good knowledge on the interactions between climatic variables and malaria can be very useful for predicting outbreaks and preparedness interventions. We investigated clinical malaria transmission patterns and its temporal relationship with climatic variables in Tubu village, Botswana. A 5-year retrospective time series data analysis was conducted to determine the transmission patterns of clinical malaria cases at Tubu Health Post and its relationship with rainfall, flood discharge, flood extent, mean minimum, maximum and average temperatures. Data was obtained from clinical records and respective institutions for the period July 2005 to June 2010, presented graphically and analysed using the Univariate ANOVA and Pearson cross-correlation coefficient tests. Peak malaria season occurred between October and May with the highest cumulative incidence of clinical malaria cases being recorded in February. Most of the cases were individuals aged >5 years. Associations between the incidence of clinical malaria cases and several factors were strong at lag periods of 1 month; rainfall (r = 0.417), mean minimum temperature (r = 0.537), mean average temperature (r = 0.493); and at lag period of 6 months for flood extent (r = 0.467) and zero month for flood discharge (r = 0.497). The effect of mean maximum temperature was strongest at 2-month lag period (r = 0.328). Although malaria transmission patterns varied from year to year the trends were similar to those observed in sub-Saharan Africa. Age group >5 years experienced the greatest burden of clinical malaria probably due to the effects of the national malaria elimination programme. Rainfall, flood discharge and extent, mean minimum and mean average temperatures showed some correlation with the incidence of clinical malaria cases.

Population dynamics and community structure of Anopheles mosquitoes along the China-Myanmar border

BACKGROUND

Understanding the ecology of malaria vectors such as species composition and population dynamics is essential for developing cost-effective strategies to control mosquito vector populations.

METHODS

Adult mosquitoes (n = 79,567) were collected in five villages along the China-Myanmar border from April 2012 to September 2014 using the CDC light trap without bait method. Mosquito community structure, Anopheles species composition and diversity were analyzed.

RESULTS

Twenty species of Anopheles mosquitoes were identified, with An. minimus s.l. accounting for 85% of the total collections. Mosquito densities varied from 0.05 females per trap per night (f/t/n) to 3.00 f/t/n, with strong seasonality in all sites and densities peaked from June to August. An. minimus s.l. was predominant (accounting for 54-91% of total captures) in four villages, An. maculatus s.l. was predominant (71%) in the high elevation village of Dao Nong, and An. culicifacies accounted for 15% of total captures in the peri-urban area of Simsa Lawk. All 20 species have been captured in the Mung Seng Yang village, 18 and 15 species in Ja Htu Kawng and Na Bang respectively, and nine species in both Simsa Lawk and Dao Nong. Species richness peaked from April to August. Species diversity, species dominance index, and species evenness fluctuated substantially from time to time with no clear seasonality, and varied greatly amongst villages.

CONCLUSIONS

Mosquitoes were abundant in the China-Myanmar bordering agricultural area with clear seasonality. Species composition and density were strongly affected by natural environments. The targeted intervention strategy should be developed and implemented so as to achieve cost-effectiveness for malaria control and elimination along the border areas.

Malaria vectors in South America: current and future scenarios

BACKGROUND

Malaria remains a significant public health issue in South America. Future climate change may influence the distribution of the disease, which is dependent on the distribution of those Anopheles mosquitoes competent to transmit Plasmodium falciparum. Herein, predictive niche models of the habitat suitability for P. falciparum, the current primary vector Anopheles darlingi and nine other known and/or potential vector species of the Neotropical Albitarsis Complex, were used to document the current situation and project future scenarios under climate changes in South America in 2070.

METHODS

To build each ecological niche model, we employed topography, climate and biome, and the currently defined distribution of P. falciparum, An. darlingi and nine species comprising the Albitarsis Complex in South America. Current and future (i.e., 2070) distributions were forecast by projecting the fitted ecological niche model onto the current environmental situation and two scenarios of simulated climate change. Statistical analyses were performed between the parasite and each vector in both the present and future scenarios to address potential vector roles in the dynamics of malaria transmission.

RESULTS

Current distributions of malaria vector species were associated with that of P. falciparum, confirming their role in transmission, especially An. darlingi, An. marajoara and An. deaneorum. Projected climate changes included higher temperatures, lower water availability and biome modifications. Regardless of future scenarios considered, the geographic distribution of P. falciparum was exacerbated in 2070 South America, with the distribution of the pathogen covering 35-46% of the continent. As the current primary vector An. darlingi showed low tolerance for drier environments, the projected climate change would significantly reduce suitable habitat, impacting both its distribution and abundance. Conversely, climate generalist members of the Albitarsis Complex showed significant spatial and temporal expansion potential in 2070, and we conclude these species will become more important in the dynamics of malaria transmission in South America.

CONCLUSIONS

Our data suggest that climate and landscape effects will elevate the importance of members of the Albitarsis Complex in malaria transmission in South America in 2070, highlighting the need for further studies addressing the bionomics, ecology and behaviours of the species comprising the Albitarsis Complex.