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Background Heterogeneity and focalization are the most common epidemiological characteristics of endemic countries in the Americas, where malaria transmission is moderate and low. During malaria elimination, the first step is to perform a risk stratification exercise to prioritize interventions. This study aimed to identify malaria risk strata in the ecoepidemiological regions of Colombia. Methods This was a descriptive and retrospective study using cumulative malaria cases in 1,122 municipalities of Colombia from 2010 to 2019. To identify the strata, the criteria proposed by PAHO were adapted. To classify the receptive areas (strata 2, 3, and 4) and nonreceptive areas (stratum 1), 1,600 m above sea level, ecotypes, main malaria vector presence, Plasmodium species prevalence and occurrence of malaria cases were used. The area occupied by the receptive municipalities, the cumulative burden, and the at-risk population in the regions were calculated. Results Ninety-one percent of the Colombian territory is receptive to the transmission of malaria and includes 749 municipalities with 9,734,271 (9,514,243–9,954,299) million at-risk inhabitants. Stratum 4 accounted for 96.7% of the malaria burden, and cases were concentrated primarily in the Pacific and Uraba-Bajo Cauca-Sinu-San Jorge regions. Plasmodium vivax predominates in most of the receptive municipalities, except in the municipalities of the Pacific region, where P. falciparum predominates. Anopheles albimanus, An. nuneztovari s.l., and An. darlingi were the main vectors in receptive areas. Conclusions In Colombia, 91.2% of the territory is receptive to the transmission of malaria and is characterized by being both heterogeneous and focused. Stratum 4 contains the greatest burden of disease, with a relatively greater proportion of municipalities with a predominance of P. vivax. However, there is a low proportion of municipalities with P. falciparum mainly in the Pacific region. These findings suggest that the latter be prioritized within the malaria elimination plan in Colombia.

Understanding the temporal dynamics (including the start, duration and end) of malaria transmission is key to optimising various control strategies, enabling interventions to be deployed at times when they can have the most impact. This temporal profile of malaria risk is intimately related to the dynamics of the mosquito populations underlying transmission. However, many outstanding questions remain surrounding these dynamics, including the specific drivers and their dependence on the ecological structure of a setting. Here we collate mosquito time-series catch data from across India in order to better understand these dynamics and the factors shaping them. Our analyses reveal pronounced heterogeneity in mosquito population dynamics, both within (across different locations) and between (in the same location) species complexes. Despite this variation, we show that these time-series can be clustered into a small number of categories characterised by distinct temporal properties and driven by a largely unique set of environmental factors. Exploration of these categories highlights that an interplay of species complex-specific factors and the ecological structure of the local environment together shape the temporal dynamics (including timing and extent of seasonality) of mosquito populations. The results of these analyses are then integrated with spatial predictions of species presence/absence in order to generate predictive maps of mosquito population seasonality across India, to inform the planning and timing of malaria control efforts. Significance Effective planning and control of malaria requires an understanding of the underlying mosquito population dynamics that determine the temporal profile of malaria risk. Here, we collate a database of monthly mosquito catch data spanning 40 years and 117 unique locations across India to explore the factors shaping these dynamics. Our analyses reveal pronounced heterogeneity in mosquito population dynamics, both within (across different locations) and across (in the same location) species complexes: this heterogeneity is driven by an interplay between species complex-specific factors and the ecological structure of the local environment. Despite this variation, the temporal patterns of mosquito abundance across these different locations can be categorised into a small number of clusters, each characterised by distinct temporal properties and each of which is influenced by a largely unique set of environmental factors. Based on these results, we create a tool to predict mosquito population seasonality in a given location, to inform the planning and timing of control efforts.

Background. Remote rural riverine villages account for most of the reported malaria cases in the Peruvian Amazon. As transmission decreases due to intensive standard control efforts, malaria strategies in these villages will need to be more focused and adapted to local epidemiology. Methods. By integrating parasitological, entomological, and environmental observations between January 2016 and June 2017, we provided an in-depth characterization of malaria transmission dynamics in 4 riverine villages of the Mazan district, Loreto department. Results. Despite variation across villages, malaria prevalence by polymerase chain reaction in March 2016 was high (>25% in 3 villages), caused by Plasmodium vivax mainly and composed of mostly submicroscopic infections. Housing without complete walls was the main malaria risk factor, while households close to forest edges were more commonly identified as spatial clusters of malaria prevalence. Villages in the basin of the Mazan River had a higher density of adult Anopheles darlingi mosquitoes, and retained higher prevalence and incidence rates compared to villages in the basin of the Napo River despite test-and-treat interventions. Conclusions. High heterogeneity in malaria transmission was found across and within riverine villages, resulting from interactions between the microgeographic landscape driving diverse conditions for vector development, housing structure, and human behavior. This work was supported by the Consejo Nacional de Ciencia, Tecnologia e Innovacion Tecnologica (grant number 008-2014-FONDECYT); the Academie de Recherche et d'Enseignement Superieur-Commission de la Cooperation au Developpement of Belgium (grant number ARES-CCD, PRD-Peru 2014-2019 to N. S., A. L. C., and A. R. A.); World Health Organization Special Programme for Research and Training in Tropical Diseases (grant number 201460655 to D. G.); and National Institutes of Health, National Institute of Allergy and Infectious Diseases (grant numbers U19AI089681 to J. M. V. and R01AI110112 to J. E. C.). A. R. A. is a Postdoctoral Researcher of the Fonds de la Recherche Scientifique (FNRS, Belgium).

Deforestation can increase the transmission of malaria. Here, we build upon the existing link between malaria risk and deforestation by investigating how the global demand for commodities that increase deforestation can also increase malaria risk. We use a database of trade relationships to link the consumption of deforestation-implicated commodities in developed countries to estimates of country-level malaria risk in developing countries. We estimate that about 20% of the malaria risk in deforestation hotspots is driven by the international trade of deforestation-implicated export commodities, such as timber, wood products, tobacco, cocoa, coffee and cotton. By linking malaria risk to final consumers of commodities, we contribute information to support demand-side policy measures to complement existing malaria control interventions, with co-benefits for reducing deforestation and forest disturbance.

Malaria remains one of the most prevalent infectious diseases in the tropics and subtropics, and Mozambique is not an exception. To design geographically targeted and effective intervention mechanisms of malaria, an up-to-date map that shows the spatial distribution of malaria is needed. This study analyzed 2018 Mozambique Malaria Indicator Survey using geostatistical methods to: i) explore individual, household, and community-level determinants of malaria in under-five children, ii) prepare a malaria prevalence map in Mozambique, and iii) produce prediction prevalence maps and exceedence probability across the country. The results show the overall weighted prevalence of malaria was 38.9% (N = 4347, with 95% CI: 36.9%–40.8%). Across different provinces of Mozambique, the prevalence of malaria ranges from 1% in Maputo city to 57.3% in Cabo Delgado province. Malaria prevalence was found to be higher in rural areas, increased with child’s age, and decreased with household wealth index and mother’s level of education. Given the high prevalence of childhood malaria observed in Mozambique there is an urgent need for effective public health interventions in malaria hot spot areas. The household determinants of malaria infection that are identified in this study as well as the maps of parasitaemia risk could be used by malaria control program implementers to define priority intervention areas.

Background: Malaria is an erstwhile public health problem that is further compounded in India due to diverse climatic conditions, ecology, socio-developmental indicators and the imminent threat of climate change. Mapping of malaria risk using climatic and ecological factors has been assessed to some extent but the vulnerability by taking into consideration the human factors i.e. socio-economics and adaptive capacity, in climatically vulnerable areas is not known. The present communication deals with determining the risk and vulnerability at district level by taking into account the hazard due to climate change, population exposed, ecological sensitivity and the community’s adaptive capacity.Methods: Based on the IPCC framework, relevant indicators for the dimensions of risk (Hazard, Exposure, Sensitivity, Adaptive Capacity) were identified from literature and was used to develop indices. Data for each indicator was obtained through government websites. Weights for different indicators were assigned using principle component analysis and the indices were aggregated to develop a district wise risk and vulnerability profile of India.Results: Malaria risk was found to be highest in Bihar (Ri=0.7) due to very low adaptive capacity coupled with high sensitivity. In some states with high climatic hazard and/or sensitivity for malaria, such as Tripura, Mizoram, Kerala and Tamil Nadu, high Adaptive capacity acted as a buffer, reducing the overall risk of malaria. Further, malaria risk is projected to be introduced for the first time in some districts of Himachal Pradesh and Arunachal Pradesh by 2030s due to the effects of climate change. Conclusion: The results of the study highlight the importance of various socio-development indicators in representing a holistic view of malaria risk. Adaptive capacity emerged as an important index as it could help counter the effects of climate change on malaria by enabling the community to cope with the adverse effects.

Solar geoengineering is often framed as a stopgap measure to decrease the magnitude, impacts, and injustice of climate change. However, the costs or benefits of geoengineering for human health are largely unknown. We project how geoengineering could impact malaria risk by comparing transmission suitability and populations-at-risk today against moderate and high emissions scenarios (RCP 4.5 and 8.5) with and without geoengineering over the next half-century. We show that if geoengineering deployment cools the tropics, it could help protect high elevation populations in eastern Africa from the encroachment of malaria, but could increase transmission in lowland sub-Saharan Africa and southern Asia. Compared to extreme warming, we also find that by 2070, geoengineering would nullify a projected reduction of nearly one billion people at risk of malaria. Our results indicate that geoengineering strategies designed to offset warming are not guaranteed to unilaterally improve health outcomes, and could produce regional trade-offs among Global South countries that are often excluded from geoengineering conversations.

The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of a mosquito. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We developed a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquitoes’ cuticle that simultaneously identifies the species and the age of three main malaria vectors, in natural populations. Using over 40,000 ecologically and genetically diverse females, we could speciate and age grade An. gambiae, An. arabiensis, and An. coluzzii with up to 95% accuracy. Further, our model learned the age of new populations with minimal sampling effort and detected the impact of control interventions on simulated mosquito populations, measured as a shift in their age structures. We anticipate our method to be applied to other arthropod vector-borne diseases.

Background:Malaria remains a major public health threat in sub-Saharan Africa. Despite efforts to eradicate the disease in Nigeria, it is still a major cause of morbidity and mortality; especially among children under five (U5) years. This study assessed individual, household, and community risk factors for malaria in Nigerian children U5 years of age.Methods:Data from the Nigerian Malaria Health Indicator Survey 2015 were pooled for analyses. This comprised of national survey of 329 clusters. Children aged 6-59 months who were tested for malaria using microscopy were retained. Multilevel logit model accounting for sampling design was used to assess individual, household and community factors associated with malaria parasitaemia. Results:A total of 5742 children were assessed for malaria parasitaemia with overall prevalence of 27% (95% CI 26-28%). Plasmodium falciparum constituted 98% of the Plasmodium species. There was no significant difference in parasitaemia between older children, and those ≤12 months. In adjusted analyses, rural living, Northwest region, household size of >7, dependence on river and rain water as primary water source were associated with higher odds of parasitaemia; while higher wealth index, all U5s who slept under bed net and dependence on packaged water were associated with lower odds of parasitemia.Conclusion:Malaria remains a substantial public health burden in Nigeria, especially in U5 children, despite sustained investment in malaria control and prevention. A quarter of the overall national burden are in the U5s. Across the six geopolitical zones, the highest burden was in children living in poorest rural households.

BackgroundAdults are frequently infected with malaria and may serve as a reservoir for further transmission, yet we know relatively little about risk factors for adult infections. In this study, we assessed malaria risk factors among adults using samples from the nationally representative, cross-sectional 2013–2014 Demographic and Health Survey (DHS) conducted in the Democratic Republic of the Congo (DRC). We further explored differences in risk factors by urbanicity.MethodsPlasmodium falciparum infection was determined by PCR. Covariates were drawn from the DHS to model individual, community and environmental-level risk factors for infection. Additionally, we used deep sequencing data to estimate the community-level proportions of drug-resistant infections and included these estimates as potential risk factors. All identified factors were assessed for differences in associations by urbanicity.ResultsA total of 16 126 adults were included. Overall prevalence of malaria was 30.3% (SE=1.1) by PCR; province-level prevalence ranged from 6.7% to 58.3%. Only 17% of individuals lived in households with at least one bed-net for every two people, as recommended by the WHO. Protective factors included increasing within-household bed-net coverage (Prevalence Ratio=0.85, 95% CI=0.76–0.95) and modern housing (PR=0.58, 95% CI=0.49–0.69). Community-level protective factors included increased median wealth (PR=0.87, 95% CI=0.83–0.92). Education, wealth, and modern housing showed protective associations in cities but not in rural areas.ConclusionsThe DRC continues to suffer from a high burden of malaria; interventions that target high-risk groups and sustained investment in malaria control are sorely needed. Areas of high prevalence should be prioritised for interventions to target the largest reservoirs for further transmission.

High levels of storage iron may increase malaria susceptibility. This risk has not been investigated in semi-immune adolescents. We investigated whether baseline iron status of non-pregnant adolescent girls living in a high malaria transmission area in Burkina Faso affected malaria risk during the following rainy season. For this prospective study, we analysed data from an interim safety survey, conducted six months into a randomised iron supplementation trial. We used logistic regression to model the risk of P. falciparum infection prevalence by microscopy, the pre-specified interim safety outcome, in relation to iron status, nutritional indicators and menarche assessed at recruitment. The interim survey was attended by 1223 (82%) of 1486 eligible participants, 1084 (89%) of whom were &lt, 20 years at baseline and 242 (22%) were pre-menarcheal. At baseline, prevalence of low body iron stores was 10%. At follow-up, 38% of adolescents had predominantly asymptomatic malaria parasitaemias, with no difference by menarcheal status. Higher body iron stores at baseline predicted an increased malaria risk in the following rainy season (OR 1.18 (95% CI 1.05, 1.34, p = 0.007) after adjusting for bed net use, age, menarche, and body mass index. We conclude that routine iron supplementation should not be recommended without prior effective malaria control.

Background: Deforestation and land use change is widespread in Madagascar, altering local ecosystems and creating opportunities for disease vectors, such as the Anopheles mosquito, to proliferate and more easily reach vulnerable, rural populations. Knowledge of risk factors associated with malaria infections is growing globally, but these associations remain understudied across Madagascar’s diverse ecosystems experiencing rapid ecological change. This study aims to uncover socioeconomic, demographic, and ecological risk factors for malaria infection across regions through analysis of a large, cross-sectional dataset.Methods: Here, we assess (1) the ecological correlates of malaria vector breeding through larval surveys, and (2) the socioeconomic, demographic, and ecological risk factors for malaria infection in four ecological regions of rural Madagascar. We determine risk factors using multilevel models for each unique ecological region of included in the study.Results: We find that regardless of the region, the presence of forms of aquatic agriculture around a community are predictive of the presence of malaria vectors. We determine that ecological and socioeconomic risk factors for malaria infection vary dramatically across study regions and range in their complexity.Conclusions: Risk factors for malaria transmission differ dramatically across regions of Madagascar. These results may help stratifying current malaria control efforts in Madagascar beyond the scope of existing interventions.

Although only a small proportion of the landmass of South Africa is classified as high risk for malaria, the country experiences on-going challenges relating to malaria outbreaks. Climate change poses a growing threat to this already dire situation. While considerable effort has been placed in public health campaigns in the highest-risk regions, and national malaria maps are updated to account for changing climate, malaria cases have increased. This pilot study considers the sub-population of South Africans who reside outside of the malaria area, yet have the means to travel into this high-risk region for vacation. Through the lens of the governmental "ABC of malaria prevention", we explore this sub-population's awareness of the current boundaries to the malaria area, perceptions of the future boundary under climate change, and their risk-taking behaviours relating to malaria transmission. Findings reveal that although respondents self-report a high level of awareness regarding malaria, and their boundary maps reveal the broad pattern of risk distribution, their specifics on details are lacking. This includes over-estimating both the current and future boundaries, beyond the realms of climate-topographic possibility. Despite over-estimating the region of malaria risk, the respondents reveal an alarming lack of caution when travelling to malaria areas. Despite being indicated for high-risk malaria areas, the majority of respondents did not use chemoprophylaxis, and many relied on far less-effective measures. This may in part be due to respondents relying on information from friends and family, rather than medical or governmental advice.

Background: Ecology of an area can greatly affects the population of the vector mosquito species by providing conditions for their breeding, thus affecting the transmission of malaria in an area. Areas having similar climate, but dissimilar ecological conditions may alter the factors of malaria transmission, thus resulting in different control strategies for disease management. Current study implies a cross-sectional approach to investigate different malaria risk factors in two ecologically distinct districts of Khyber Pakhtunkhwa, Pakistan. Methods: Data was collected by using closed-ended questionnaire from 2854 and 1944 randomly selected individuals of District Charsadda and Swabi respectively from December to August. A univariate logistic regression analysis was used to measure the association between history of malaria infection and different possible risk factors.Results: 37.6% of the respondents in Charsadda and 46.5% in Swabi were having history of malaria infection. Plasmodium vivax was found to be the dominant species causing infection in the local population. In district Swabi, factors like windows mesh, drinking water source, irrigated fields, sleeping habits, gender and marital status was found significant (PPConclusions: This study provides some basic guidelines by identifying the potential risk factors in districts having diverse ecology to design a comprehensive and effective control strategy to reduce malaria disease burden.

BackgroundReports of P. vivax infections among Duffy-negative hosts have begun to accumulate throughout sub-Saharan Africa. Despite this growing body of evidence, no nationally representative epidemiological surveys of P. vivax in sub-Saharan Africa nor population genetic analyses to determine the source of these infections have been performed.MethodsTo overcome this critical gap in knowledge, we screened nearly 18,000 adults in the Democratic Republic of the Congo (DRC) for P. vivax using samples from the 2013-2014 Demographic Health Survey. Infections were identified by quantitative PCR and confirmed with nested-PCR. P. vivax mitochondrial genomes were constructed after short-read sequencing. Risk factors, spatial distributions and population genetic analyses were explored.FindingsOverall, we found a 2.96% (95% CI: 2.28%, 3.65%) prevalence of P. vivax infections across the DRC. Nearly all infections were among Duffy-negative adults (486/489). Infections were not associated with typical malaria risk-factors and demonstrated small-scale heterogeneity in prevalence across space. Mitochondrial genomes suggested that DRC P. vivax is an older clade that shares its most recent common ancestor with South American isolates.InterpretationP. vivax is more prevalent across the DRC than previously believed despite widespread Duffy-negativity. Comparison to global and historical P. vivax sequences suggests that historic DRC P. vivax may have been transported to the New World on the wave of European expansion. Our findings suggest congolese P. vivax is an innocuous threat given its relatively flat distribution across space, lack of malaria risk factors, and potentially ancestral lineage.FundingNational Institutes of Health and the Wellcome Trust.

Malaria has long been a cause of human suffering and mortality in Sub-Saharan Africa, particularly in Ethiopia. The objective of this study was to assess the spatial distribution of malaria risk using MCE (Multi-Criteria Evaluation). To analyze and generate a spatial malaria risk level distribution map. Factors that affect the spatial malaria hazard and risk distribution have been considered. Such as; temperature, rainfall, altitude, slope, distance from the river, population density, and land use land cover factors were selected to produce a malaria risk map of the Didessa district area. GIS based multi-criteria evaluation method applied using weighted overlay analysis by considering three map layer factors (i.e. malaria hazard map layer, element at risk map layer, and vulnerability map layer), an optimum malaria risk map is produced. The malaria risk map result shows that 0.68%, 36.2%, 30.1%, 27.52% 5.5% of the study area falls under very high, high, moderate, low, and very low spatial malaria risk levels respectively. Our findings indicate that malaria is heavily influenced by major environmental parameters and socio-economic factors and these factors play a vital role either directly or indirectly in the occurrence of this vector-borne disease. In conclusion, 36.88% (31034.88 ha) of the study area has a high potential risk of malaria disease manifestation and occurrence. The result of this report indicates that there are high malaria risk areas in the district. This ascertains the communities living in those areas are prone to the disease. Therefore, there is an urgent need to prevent and progressively reduce malaria disease distribution through policy formulation and health care implementation in prioritized areas. This study is useful to use as a guideline for further research study in combating malaria distribution, particularly in developing countries.

Deforestation can increase the transmission of malaria. Here, we build upon the existing link between malaria risk and deforestation by investigating how the global demand for commodities that increase deforestation can also increase malaria risk. We use a database of trade relationships to link the consumption of deforestation-implicated commodities in developed countries to estimates of country-level malaria risk in developing countries. We estimate that about 20% of the malaria risk in deforestation hotspots is driven by the international trade of deforestation-implicated export commodities, such as timber, wood products, tobacco, cocoa, coffee and cotton. By linking malaria risk to final consumers of commodities, we contribute information to support demand-side policy measures to complement existing malaria control interventions, with co-benefits for reducing deforestation and forest disturbance., Because many primary commodities cause deforestation and deforestation can increase malaria transmission, international trade can thus indirectly influence malaria risk. Here the authors use trade databases for commodites associated with deforestation to demonstrate that consumption of such commodities in developed nations could increase malaria risk in developing nations.

Background: Erer is one of the districts in Ethiopia where the first malaria transmission season occurs. Although the focus on malaria research has increasingly gained ground, little emphasis has been given to develop quantitative methods for assessing malaria hazard and risk in a temporal and spatial perspective. Objective: To characterize and examine the temporal and spatial malaria trend. The research also aims at producing a predictive model of malaria hazard and risk in Erer district. Methods: In this study a cross sectional research design was used. It was carried out through the collection of both quantitative and qualitative data about the nature of malaria and household’s response towards it. A multi-stage sampling method was used and 136 sample size was determined from the sampling frame of 6203 households. Simple descriptive analysis technique was used to determine the malaria trend of the district. Integration of Geographic information system and analytic hierarchy process was used to determine the weight of each factor pair wise comparison and weighted linear combination was used to aggregate and produce the hazard and malaria risk maps. Results: Results have shown that 19.92%, 27.96%, 32.35%, 18.93% and 0.82% of the district was very high, high, moderate, low and very low malaria risk areas respectively. The malaria trend of the area was found to be variable across time with 2014 the peak year while the minimum case observed was in 2016. Conclusion: It is possible to conclude that risk maps are important for estimating the scale of the risk, and enable detection of high risk areas, thus facilitating decision making and policy formulation for enhanced malaria control interventions. Key words: Analytic Hierarchy Process; Malaria risk; Malaria trend; Weighted overlay

Malaria is one of the most killer diseases in our planet earth. It is the primary cause of patient talks, admissions and demise every year in Ethiopia. It is important to reduce the transmission and effects of the problem identifying and mapping the malaria risk area using geospatial technologies. The general objective of the study is to analyze and map malaria prone areas using geospatial tools in Amibara and Gewanie Woredas of Afar Region. The specific objectives are to identify parameters that contribute to malaria hazard in the study area and to classify and map areas at risk of malaria. To achieve the stated objectives, the researchers employed spatial modeling technique particularly Weighted Overlay Analysis (WOA). Climatic data, topographical data and infrastructural data are the major data of the study. These data were processed and analyzed by ArcGIS, Erdas imagine, Idrisi, 3DEM terrain visualization and global mapper software. The result showed that the area is suitable moderately and marginally for malaria. From the total area of the study, 35 and 65% were moderately and marginally suitable for the occurrence of malaria respectively. Therefore, as malaria is one of the killer diseases the concerned bodies should use the technologies to prevent and control the transmission of malaria in the study area and there should be health package to prevent the diseases. Key words: Malaria mapping, geospatial tools.

Malaria burden has considerably declined in the last 15 years mainly due to large-scale vector control. The continued decline can be sustained through malaria risk stratification. Malaria stratific...

Objectives Malaria remains a public health concern worldwide, including Indonesia. Purworejo is a district in which endemic of malaria, they have re-setup to entering malaria elimination in 2021. Accordingly, actions must be taken to accelerate and guaranty that the goal will reach based on an understanding of the risk factors for malaria. Thus, we analysed malaria risk factors based on human and housing conditions in Kaligesing, Purworejo, Indonesia. Methods A case-control study was carried out in Kaligesing subdistrict, Purworejo, Indonesia in July to August 2017. A structured questionnaire and checklist were used to collect data from 96 participants, who consisted of 48 controls and 48 cases. Univariate, bivariate, and multivariate analyses were performed. Results Bivariate analysis found that education level, the presence of a cattle cage within 100 m of the house, not sleeping under a bednet the previous night, and not closing the doors and windows from 6 p.m. to 5 a.m. were significantly (p≤0.25) associated with malaria. Of these factors, only not sleeping under a bednet the previous night and not closing the doors and windows from 6 p.m. to 5 a.m. were significantly associated with malaria. Conclusions The findings of this study demonstrate that potential risk factor for Malaria should be paid of attention all the time, particularly for an area which is targeting Malaria elimination.

The Cox proportional hazards (PH) model with time-dependent covariates has been widely used in medical and health related studies to examine the impacts of time-varying risk factors on survival. Th...

Background The Democratic Republic of the Congo (DRC) remains one of the countries most impacted by malaria despite decades of control efforts, including multiple mass insecticide treated net (ITN) distribution campaigns. The multi-scalar and complex nature of malaria necessitates an understanding of malaria risk factors over time and at multiple levels (e.g., individual, household, community). Surveillance of households in both rural and urban settings over time, coupled with detailed behavioral and geographic data, enables the detection of seasonal trends in malaria prevalence and malaria-associated behaviors as well as the assessment of how the local environments within and surrounding an individual's household impact malaria outcomes. Methods Participants from seven sites in Kinshasa Province, DRC were followed for over two years. Demographic, behavioral, and spatial information was gathered from enrolled households. Malaria was assessed using both rapid diagnostic tests (RDT) and polymerase chain reaction (PCR) and seasonal trends were assessed. Hierarchical regression modeling tested associations between behavioral and environmental factors and positive RDT and PCR outcomes at individual, household and neighborhood scales. Results Among 1591 enrolled participants, malaria prevalence did not consistently vary seasonally across the sites but did vary by age and ITN usage. Malaria was highest and ITN usage lowest in children ages 6–15 years across study visits and seasons. Having another member of the household test positive for malaria significantly increased the risk of an individual having malaria [RDT: OR = 4.158 (2.86–6.05); PCR: OR = 3.37 (2.41–4.71)], as did higher malaria prevalence in the 250 m neighborhood around the household [RDT: OR = 2.711 (1.42–5.17); PCR: OR = 4.056 (2.3–7.16)]. Presence of water within close proximity to the household was also associated with malaria outcomes. Conclusions Taken together, these findings suggest that targeting non-traditional age groups, children >5 years old and teenagers, and deploying household- and neighborhood-focused interventions may be effective strategies for improving malaria outcomes in high-burden countries like the DRC.

To assess the importance of malaria risk factors for children in sub-Saharan African countries. 61,292 children of 16 countries from DHS and MIS surveys were included in analysis. A regression model with analyse of variance and plots of mean decrease accuracy and mean decrease Gini indices were performed. The most important risk factor was the country and it contributed 52.33 % to the variance of the model. Wealth status of the child's family was the first socio-economic factor which contributed more to the difference of malaria risk among African children. There was no geographic factor among the five most important variables. Quantity of precipitation was the sixth most important factor. Our study has the potential for driving control effort in the fight against malaria in the continent which represent the majority of global malaria cases. The study indicates that, when implementing health policies, community characteristics must be taken into account., SCOPUS: ar.j, info:eu-repo/semantics/published

Malaria is one of the largest problems threatening global public health that is expected to increase in the future under climate change due to associated warming and wetter conditions. This will exacerbate disease burden in Burundi as one of sub-Saharan African countries, where 2 million cases of malaria were reported in 2015. This highlights the need for developing a methodology for mapping malaria risk under climate change and delineating those regions that may potentially experience malaria epidemics in the future. Malaria transmission and distribution are generally determined by a wide range of climatic, topographic and socioeconomic factors. The paper in hand is intended to map malaria risk in Burundi under climate change up to 2050. For this purpose, a GIS-based model was developed for mapping malaria as a function of various climatic and topographic determinants of malaria. The developed GIS-model was used in mapping malaria risk under current climatic conditions. Thereafter, the produced risk map was validated compared to malaria morbidity data in Burundi at health district level. Finally, the GIS-model was applied to map malaria risk in the future under RCPs 2.6 and 8.5 scenarios up to 2050. It was found that about 34.6% and 44% of Burundi land surface will be highly vulnerable to malaria risk by 2050 under RCPs 2.6 and 8.5 scenario, respectively. Also, it was noted that such highly vulnerable areas are distributed mainly in northern parts of the country. The suggested GIS-based model for mapping malaria risk under climate change can contribute largely to more informed decision-making and policy making process in terms of planning for intervention and control malaria risk. This in turn can support reducing disease burden and improving resilience to climate change.

In order to determine the geography of malaria, to know the spatial distribution of health facilities and to identify malaria risk areas in LibrevilleOwendo-Akanda area, a study was conducted from july 2016 to june 2017. This study consisted of collecting hospital morbidity data and characterizing public and private health structures in the study area. The malaria data were collected in the clinics using the pre-designed and tested collection forms. Moreover, the cartography of the health structures and chiefdoms of the different neighborhoods from which each patient came was made using a GPSMAP 62s (Garmin). The spatial analyzes were done in ArcGIS 10.3 software according to the Hot Spot model, to identify areas at risk of malaria. Thus, 211 private and public health structures divided into 12 types were identified. These structures are unequally distributed in the LibrevilleOwendo-Akanda region. The best represented are the private medical surgeries (n = 62 ; 29.38%), the drugstores (n = 39 ; 18.48%), the structure for nursing treatment (n = 36 ; 17.06%). On the other hand, the least represented are the medical centers (n = 2 ; 0.95%) and childbirth clinics (n = 1 ; 0.47%). Finally, the spatial analyzes made it possible to show that the clinical malaria prevalence is higher (hot spots) in the districts of the commune of Libreville than in the neighborhoods of the two other neighboring communes that are Owendo and Akanda. The results obtained contribute to make known the spatial distribution of health facilities and hot spots of malaria in the area of Libreville-Owendo-Akanda.Afin de determiner la geographie du paludisme, de connaitre la distribution spatiale des formations sanitaires et d’identifier les zones a risque palustre dans la region de Libreville-Owendo-Akanda, une etude a ete menee de juillet 2016 a juin 2017. Cette etude a consiste a recolter les donnees de morbidite palustre hospitaliere et a caracteriser les structures sanitaires publiques et privees de ladite zone. Les donnees de paludisme ont ete collectees dans les cliniques au moyen des fiches de collecte pre-concues et testees. De plus, la cartographie des structures de sante et des chefferies des differents quartiers, d’ou provenait chaque malade, s’est faite a l’aide d’un GPS-MAP 62s de marque Garmin. Des analyses spatiales ont ete faites dans ArcGIS 10.3 selon le modele des Hot Spots, pour identifier les zones a risques du paludisme. Ainsi, 211 structures privees et publiques de sante reparties en 12 types ont ete repertoriees. Ces structures sont inegalement reparties dans la region de Libreville-Owendo-Akanda. Les mieux representes sont les cabinets medicaux prives (n=62 ; 29,38%), les pharmacies (n=39 ; 18,48%), les cabinets des soins infirmiers (n=36 ; 17,06%). Tandis que les moins representes sont les centres medicaux (n=2 ; 0,95%) et les cabinets d’accouchement (n=1 ; 0,47%). Enfin, les analyses spatiales faites ont permis de montrer que la prevalence palustre clinique est plus elevee (hot spots) dans les quartiers de la commune de Libreville que dans les quartiers des deux autres communes voisines que sont Owendo et Akanda. Les resultats obtenus concourent a faire connaitre la distribution spatiale des formations sanitaires et des hot spots du paludisme dans la region de Libreville-Owendo-Akanda.

Pregnant women and children are the most vulnerable populations for malaria infection. Yet, knowledge of risk, and preventive measures are poor among this population. Using the 2015 Nigeria Malaria Indicator Survey, we applied logit link function to estimate the associations of wealth status, educational attainment, and region of residence with malaria risk knowledge and prevention strategies (using a treated mosquito net and malaria drugs) among 739 Nigerian pregnant women aged 15-49 years. Urban women who had obtained a secondary school education (Adjusted odds ratio [aOR] = 2.12; 95% confidence interval [CI] 1.09-4) or higher (aOR = 8.31; 95% CI 3.2-22) had more knowledge of malaria risk. Urban women in the South-West (aOR = 5.02; [CI] 2.02-12.50) and South-East (aOR = 2.68; 95% CI 1.19-6.06) were more likely to use treated mosquito nets during pregnancy. Women in the urban South-West (aOR = 4.04; 95% CI 1.5-11) were more likely to use malaria drugs during pregnancy than those in the North-Central. A wide regional disparity in the knowledge of malaria risks and use of preventive measures exists. Thus, promoting equal access to malaria preventive measures as well as improving knowledge about malaria transmission by mosquitoes should be considered as essential components of ongoing malaria control and elimination efforts in Nigeria.

The rate of malaria transmission is strongly determined by parasite development time in the mosquito, known as the extrinsic incubation period (EIP), since the quicker parasites develop, the greater the chance that the vector will survive long enough for the parasite to complete development and be transmitted. EIP is known to be temperature-dependent but this relationship is surprisingly poorly characterized. There is a single degree-day model for EIP of Plasmodium falciparum that derives from a limited number of poorly controlled studies conducted almost a century ago. Here, we show that the established degree-day model greatly underestimates the rate of development of P. falciparum in both Anopheles stephensi and An. gambiae mosquitoes at temperatures in the range of 17–20°C. We also show that realistic daily temperature fluctuation further speeds parasite development. These novel results challenge one of the longest standing models in malaria biology and have potentially important implications for understanding the impacts of future climate change.

Environmental changes related to agricultural practices and activities can impact malaria transmission. In the objective to evaluate this impact on the human-vector contact, the level of human exposure to Anopheles vector bites was assess by an immuno-epidemiological indicator based on the assessment of the human IgG antibody response to the Anopheles gambiae gSG6-P1 salivary peptide, previously validated as a pertinent biomarker.Two cross-sectional surveys were carried out in the dry and rainy season in three villages with intensive agricultural plantations (N'Zikro with rubber cultivation, Ehania-V5 and Ehania-V1 with palm oil exploitation) and in a control village without plantations (Ayébo). Overall, 775 blood samples were collected in filter papers from children aged 1 to 14 years-old for immunological analysis by ELISA. The IgG levels to the gSG6-P1 salivary peptide significantly differed between studied villages both in the dry and the rainy seasons (P

Introduction Fever in the returned traveller is a common presentation. Traditionally, guidelines suggest three blood films to rule out malaria however with advances in rapid diagnostic tests (RDTs), sensitivities have improved. RDTs were introduced in 2015 as part of malaria screening in our hospital. Recent data suggest that in travellers to low risk countries, in whom there is a low clinical suspicion of malaria, a single negative RDT issufficient to exclude malaria. We audited our current diagnostic approach with the aim of rationalising our guideline. Methods The audit was approved by the local clinical governance department. All children who received malaria screens between 1 st January 2015 and 31 st December 2017 were identified and retrospective data collected from electronic patient records. Data collected included; demographics; type, number of malaria screen and their results; duration and destination of travel. Risk of malaria was stratified in accordance with the NHS fitfortravel website and Centres for Disease Control data. Statistical analysis was carried out using Microsoft Excel. Results Between 2015–2017, 132 children were investigated for malaria. The median age was 3 years old. 53% of the children (n=70) travelled to a low risk country, mostly to Bangladesh (n=63). Two cases of falciparum malaria were diagnosed from travellers to high risk areas in Africa. RDTs were consistently carried out from August 2017 on all initial malaria screens with blood-film. Only 31% of tests complied with the guideline, receiving 3 malaria screens. 48% received 1 screen and 21% received 2 screens. There was no correlation between the number of screens and whether malaria was the most likely diagnosis (Fisher’s Exact test p=0.15) or the malaria risk in the destination of travel (p=0.36). Conclusion Malaria was very rare in our population. Most travellers returned from a destination of low risk. There was a lack of consistency in the number of malaria screens performed. We proposed a modified guideline based on risk stratification from the destination of travel and the clinical presentation of the child. We encourage clinicians to consider other causes of fever in well children who travelled to low risk countries and suggest a single RDT with blood film would be sufficient to rule out malaria in this group.

Geospatial statistical models play an important role in malaria control and prevention; they are widely used to produce malaria risk maps, which are essential to guide efficient resource allocation for intervention. Although many models are available for spatial mapping, the most commonly used model in the literature is the Bayesian geostatistical model (BGM), which is based on an underlying Gaussian process. To our knowledge, methods such as splines and decision trees ensemble methods have not been compared relative to their predictive skill for country level malaria prevalence mapping. Moreover, as more countries now have multiple datasets collected throughout the past decade, it is critical to evaluate if the inclusion of past datasets and the use of spatio-temporal models improve the prediction accuracy of present spatial distribution of malaria. Here we compare the prediction accuracy of five models under spatial and spatio-temporal settings in five African countries. The five models are stepwise logistic regression, generalized additive model (GAM), gradient boosted trees (GBM), Bayesian additive regression trees (BART) and the BGM. There is not a single best model to predict malaria prevalence on a national scale. The model performances varied from country to country, and from spatial to spatio-temporal setting. In general, BGM, GAM and BART models performed well, with BGM being the most consistent. The inclusion of past data is not always beneficial: the predictive performance of GAM and GBM increased under spatio-temporal setting, but BGM’s performance decreased in most of the countries. An accurate depiction of malaria risk is important and statistical assumptions that are suitable for a country does not always fit other countries and a wide range of models and settings should be used. It ensures that we find the best modeling approach possible and can provide additional insight to the spatial distribution of malaria risk.Author summaryMalaria is still affecting hundreds of millions of people every year, and killing hundreds of thousands. As the majority of malaria intervention and control policies are developed at the national level, accurate spatial prediction of malaria risk is important. Choosing the best modeling approach for prediction is not straightforward. Here we compare the predictive performance of five models in five countries, with and without dataset from multiple surveys, to provide empirical evidence on whether there is a single best model for national level malaria prediction, and whether inclusion of past dataset may be beneficial in predicting current distribution of malaria risk. We find that models’ performances vary from country to country and there is no single best model. Although Bayesian geostatistical model is widely and commonly used in the literatures, its performance is not necessarily superior to other simpler-to-fit methods such as general additive model (splines) and Bayesian additive regression tree. Importantly, we also show that the incorporation of past data does not always improve spatial predictions of current disease risk. Together, this demonstrate the importance of fitting wide range of models as part of the prediction mapping process, instead of relying on a one-size-fit-all model.

Background Long-lasting insecticidal nets (LLINs) are one of the main vector control strategies recommended by the World Health Organization for the control and elimination of malaria. This study aimed to evaluate the use and retention of LLINs during the 5 years of implementing an integrated control strategy in a malaria-endemic area in the Brazilian Amazon. Methods This intervention study was conducted in localities of the municipality of Barcelos, Amazonas, from 2008 to 2014. Four rural localities situated along the Padauiri River were the object of this study. Two localities (Bacabal-rio Aracá and the São Sebastião district) were used as controls. LLINs were distributed to all residents of the Padauiri River; assessments were made regarding their use and retention via a semistructured questionnaire, a household register, and direct observation during 5 years. Results Overall, 208 individuals participated in the study. In the baseline pilot study (2008), 9.9% of the subjects in the intervention group had slept with mosquito nets the previous night compared with 37.8% of the subjects in the control group. In 2010, this percentage was 43.2% in the intervention group and 50.9% in the control group. Therefore, 1 year after the implementation of the strategy, although there was an increase in the use of mosquito nets in both groups, this increase was significantly higher in the intervention group. This increase in LLINs use did not persist after 5 years of intervention. The households’ evaluation in 2014 showed that 80% of the houses in the intervention group owned at least one LLIN compared with 66% in the control group (p = 0.11); 76% of households in the intervention group owned sufficient LLINs for all family members compared with 50% in the control group (p = 0.007). Conclusions High ownership and retention of the LLINs was observed in the intervention group. At 1 year after the distribution of these LLINs, there was a significant increase in their use that was not maintained over the long term. Control strategies must be permanent; however, exploring new strategies is necessary to ensure that the knowledge acquired further modifies the attitudes and behaviours.

Background Bayesian methods have been used to generate country-level and global maps of malaria prevalence. With increasing availability of detailed malaria surveillance data, these methodologies can also be used to identify fine-scale heterogeneity of malaria parasitaemia for operational prevention and control of malaria. Methods In this article, a Bayesian geostatistical model was applied to six malaria parasitaemia surveys conducted during rainy and dry seasons between November 2010 and 2013 to characterize the micro-scale spatial heterogeneity of malaria risk in northern Ghana. Results The geostatistical model showed substantial spatial heterogeneity, with malaria parasite prevalence varying between 19 and 90%, and revealing a northeast to southwest gradient of predicted risk. The spatial distribution of prevalence was heavily influenced by two modest urban centres, with a substantially lower prevalence in urban centres compared to rural areas. Although strong seasonal variations were observed, spatial malaria prevalence patterns did not change substantially from year to year. Furthermore, independent surveillance data suggested that the model had a relatively good predictive performance when extrapolated to a neighbouring district. Conclusions This high variability in malaria prevalence is striking, given that this small area (approximately 30 km × 40 km) was purportedly homogeneous based on country-level spatial analysis, suggesting that fine-scale parasitaemia data might be critical to guide district-level programmatic efforts to prevent and control malaria. Extrapolations results suggest that fine-scale parasitaemia data can be useful for spatial predictions in neighbouring unsampled districts and does not have to be collected every year to aid district-level operations, helping to alleviate concerns regarding the cost of fine-scale data collection. Electronic supplementary material The online version of this article (10.1186/s12936-019-2703-4) contains supplementary material, which is available to authorized users.

Background Malaria remains a major challenge in sub-Saharan Africa and Ghana is not an exception. Effective malaria transmission control requires evidence-based targeting and utilization of resources. Disease risk mapping provides an effective and efficient tool for monitoring transmission and control efforts. The aim of this study is to analyse and map malaria risk in children under 5 years old, with the ultimate goal of identifying areas where control efforts can be targeted. Methods Data collected from the 2016 Ghana demographic and health survey was analyzed. Binomial logistic regression was applied to examine the determinants of malaria risk among children. Model-based geostatistical methods were applied to analyze, predict and map malaria prevalence. Results There is a significant association of malaria prevalence with area of residence (rural/urban), age, indoor residual spray use, social economic status and mother’s education level. Overall, parasitaemia prevalence among children under 5 years old for the year 2016 is low albeit characterized by “hotspots” in specific areas. Conclusion The risk maps indicate the spatial heterogeneity of malaria prevalence. The high resolution maps can serve as an effective tool in the identification of locations that require targeted interventions by programme implementers; this is key and relevant for reducing malaria burden in Ghana.

Maps of infection risk are a vital tool for the elimination of malaria. Routine surveillance data of malaria case counts, often aggregated over administrative regions, is becoming more widely available and can better measure low malaria risk than prevalence surveys. However, aggregation of case counts over large, heterogeneous areas means that these data are often underpowered for learning relationships between the environment and malaria risk. A model that combines point surveys and aggregated surveillance data could have the benefits of both but must be able to account for the fact that these two data types are different malariometric units. Here, we train multiple machine learning models on point surveys and then combine the predictions from these with a geostatistical disaggregation model that uses routine surveillance data. We find that, in tests using data from Colombia and Madagascar, using a disaggregation regression model to combine predictions from machine learning models trained on point surveys improves model accuracy relative to using the environmental covariates directly.

Multilevel models have long been used by health geographers working on questions of space, place, and health. Similarly, health geographers have pursued interests in determining whether or not the effect of an exposure on a health outcome varies spatially. However, relatively little work has sought to use multilevel models to explore spatial variability in the effects of a contextual exposure on a health outcome. Methodologically, extending multilevel models to allow intercepts and slopes to vary spatially is straightforward. The purpose of this paper, therefore, is to show how multilevel spatial models can be extended to include spatially varying covariate effects. We provide an empirical example on the effect of agriculture on malaria risk in children under 5 years of age in the Democratic Republic of Congo.

IntroductionMalawi’s malaria burden is primarily assessed via cross-sectional national household surveys. However, malaria is spatially and temporally heterogenous and no analyses have been performed at a subdistrict level throughout the course of a year. The WHO recommends mass distribution of long-lasting insecticide-treated bed nets (LLINs) every 3 years, but a national longitudinal evaluation has never been conducted in Malawi to determine LLIN effectiveness lifespans.MethodsUsing District Health Information Software 2 (DHIS2) health facility data, available from January 2018 to June 2020, we assessed malaria risk before and after a mass distribution campaign, stratifying by age group and comparing risk differences (RDs) by LLIN type or annual application of indoor residual spraying (IRS).Results711 health facilities contributed 20 962 facility reports over 30 months. After national distribution of 10.7 million LLINs and IRS in limited settings, malaria risk decreased from 25.6 to 16.7 cases per 100 people from 2018 to 2019 high transmission seasons, and rebounded to 23.2 in 2020, resulting in significant RDs of −8.9 in 2019 and −2.4 in 2020 as compared with 2018. Piperonyl butoxide (PBO)-treated LLINs were more effective than pyrethroid-treated LLINs, with adjusted RDs of −2.3 (95% CI −2.7 to −1.9) and −1.5 (95% CI −2.0 to −1.0) comparing 2019 and 2020 high transmission seasons to 2018. Use of IRS sustained protection with adjusted RDs of −1.4 (95% CI −2.0 to −0.9) and −2.8% (95% CI −3.5 to −2.2) relative to pyrethroid-treated LLINs. Overall, 12 of 28 districts (42.9%) experienced increases in malaria risk in from 2018 to 2020.ConclusionLLINs in Malawi have a limited effectiveness lifespan and IRS and PBO-treated LLINs perform better than pyrethroid-treated LLINs, perhaps due to net repurposing and insecticide-resistance. DHIS2 provides a compelling framework in which to examine localised malaria trends and evaluate ongoing interventions.

Mozambique is a country in Southern Africa with around 30 million inhabitants. Malaria is the leading cause of mortality in the country. According to the WHO, Mozambique has the third highest number of malaria cases in the world, representing approximately 5% of the world total cases. Sussundenga District has the highest incidence in the Manica province and environmental conditions are the major contributor to malaria transmission. There is a lack of malaria risk maps to inform transmission dynamics in Sussundenga village. This study develops a malaria risk map for Sussundenga Village in Mozambique and identifies high risk areas to inform on appropriate malaria control and eradication efforts. One hundred houses were randomly sampled and tested for malaria in Sussundenga Rural Municipality. To construct the map, a spatial conceptual model was used to estimate risk areas using ten environmental and anthropic factors. Data from Worldclim, 30 × 30 Landsat images were used, and layers were produced in a raster data set. Layers between class values were compared by assigning numerical values to the classes within each layer of the map with equal rank. Data set input was classified, using diverse weights depending on their appropriateness. The reclassified data outputs were combined after reclassification. The map indicated a high risk for malaria in the northeast and southeast, that is, the neighborhoods of Nhamazara, Nhamarenza, and Unidade. The central eastern areas, that is, 25 de Junho, 1 and 2, 7 de Abril, and Chicueu presented a moderate risk. In Sussundenga village there was 92% moderate and 8% high risk. High malaria risk areas are most often located in densely populated areas and areas close to water bodies. The relevant findings of this study can inform on effective malaria interventions.

Residual transmission is the persistence of malaria transmission after scale-up of appropriate vector control tools and is one of the key challenges for malaria elimination today. Although long associated with outdoor biting, other mosquito behaviours such as partly feeding upon animals contribute greatly to sustaining transmission. Peri-domestic livestock can be used as decoy to protect humans from blood-seeking vectors but this approach often leads to an increased malaria risk in a phenomenon known as zoopotentiation. Treating the said livestock with drugs capable of killing intestinal parasites as well as mosquitoes that feed upon them has the potential to tackle malaria through a previously unexplored mechanism. The advantages and challenges associated with this approach are briefly discussed here. Numerous references are purposely provided. This article is part of the theme issue ‘Novel control strategies for mosquito-borne diseases’.