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

Abstruse Side of Climate Change, Impact on Malaria: A Systematic Evidence Review Comparing Iran versus Globally

Background

Infectious outbreaks due to disrupted social and environmental conditions after climate change-induced events complicate disasters. This research aimed to determine the contentions of bioclimatic variables and extreme events on the prevalence of the most common Climate-Sensitive Infectious Disease (CSID); Malaria in Iran.

Methods

The present narrative systematic review study was conducted on the bioclimatic variable impact on the prevalence of malaria, as a common CSID. The search was conducted in 3 sections: global climate change-related studies, disaster related, and studies that were conducted in Iran. The literature search was focused on papers published in English and Persian from Mar 2000 to Dec 2021, using electronic databases; Scopus, Web of Science, PubMed, Google Scholar, SID, Magiran, and IranDoc.

Results

Overall, 41 studies met the inclusion criteria. The various types of climatic variables including; Temperature, rainfall, relative humidity, and hydrological events including; flood, drought, and cyclones has been reported as a predictor of malaria. The results of studies, inappropriately and often were inconsistent in both Iran and other parts of the world.

Conclusion

Identifying malaria outbreak risks is essential to assess vulnerability, and a starting point to identify where the health system is required to reduce the vulnerability and exposure of the population. The finding of most related studies is not congruent to achieve reliable information, more extensive studies in all climates and regions of the country, by climatic models and high accuracy risk map, using the long period of bioclimatic variables and malaria trend is recommended.

Evaluation of the malaria surveillance system - Adaklu District, Volta Region, Ghana, 2019

Objectives

We evaluated the malaria surveillance system in Adaklu District of the Volta Region of Ghana to determine if the system was meeting its objectives and assessed its usefulness and attributes.

Study design

Descriptive cross-sectional design was used in evaluating the surveillance system.

Methods

We interviewed stakeholders using a semi-structured questionnaire on case detection and reporting. We assessed the system attributes using the Centers for Disease Control and Prevention updated guidelines for evaluating public health surveillance systems. We extracted and reviewed malaria surveillance data from the District Health Management Information System 2. Summary statistics and direct content analysis were performed on quantitative and qualitative data respectively.

Results

Of the 80,441 suspected malaria cases recorded in Adaklu District from 2014 to 2018, 47,917 (59.6%) cases were confirmed. The system was meeting its objective of detecting malaria cases and monitoring trends in the population however, the system missed an epidemic in August 2016. Data generated from the surveillance system is used by the NMCP to aid in the distribution of logistics such as LLINs, RDT test kits, and track malaria control progress in the district. Staff at all levels were able to detect, confirm, treat and report malaria. All sub-districts/health facilities reported to the district and reports were all accurate and timely. The predictive value positive was 62.9%.

Conclusions

The malaria surveillance system in Adaklu District was useful and meeting its set objective of monitoring trends of malaria in the population. It was simple, flexible, acceptable and representative; however, the system was not detecting epidemics. The District Health Management Team should set alert and epidemic thresholds to help detect promptly epidemics of malaria in the district.

Public health priorities for Sino-Africa cooperation in Eastern Africa in context of flooding and malaria burden in Children: a tridecadal retrospective analysis

BACKGROUND

Malaria remains a major public health burden to children under five, especially in Eastern Africa (E.A), -a region that is also witnessing the increasing occurrence of floods and extreme climate change. The present study, therefore, explored the trends in floods, as well as the association of their occurrence and duration with the malaria incidence in children < 5 years in five E.A partner countries of Forum for China-Africa Cooperation (FOCAC), including Ethiopia, Kenya, Somalia, Sudan, and Tanzania between 1990 and 2019.

METHODS

A retrospective analysis of data retrieved from two global sources was performed: the Emergency Events Database (EM-DAT) and the Global Burden of Diseases Study (GBD) between 1990 and 2019. Using SPSS 20.0, a correlation was determined based on ρ= -1 to + 1, as well as the statistical significance of P = < 0.05. Time plots of trends in flooding and malaria incidence were generated in 3 different decades using R version 4.0.

RESULTS

Between 1990 and 2019, the occurrence and duration of floods among the five E.A partner countries of FOCAC increased and showed an upward trend. On the contrary, however, this had an inverse and negative, as well as a weak correlation on the malaria incidence in children under five years. Only Kenya, among the five countries, showed a perfect negative correction of malaria incidence in children under five with flood occurrence (ρ = -0.586**, P-value = 0.001) and duration (ρ = -0.657**, P-value = < 0.0001).

CONCLUSIONS

This study highlights the need for further research to comprehensively explore how different climate extreme events, which oftentimes complement floods, might be influencing the risk of malaria in children under five in five E.A malaria-endemic partner countries of FOCAC. Similarly, it ought to consider investigating the influence of other attributes apart from flood occurrence and duration, which also compound floods like displacement, malnutrition, and water, sanitation and hygiene on the risk and distribution of malaria and other climate-sensitive diseases.

Malaria, climate variability, and interventions: modelling transmission dynamics

Assessment of the relative impact of climate change on malaria dynamics is a complex problem. Climate is a well-known factor that plays a crucial role in driving malaria outbreaks in epidemic transmission areas. However, its influence in endemic environments with intensive malaria control interventions is not fully understood, mainly due to the scarcity of high-quality, long-term malaria data. The demographic surveillance systems in Africa offer unique platforms for quantifying the relative effects of weather variability on the burden of malaria. Here, using a process-based stochastic transmission model, we show that in the lowlands of malaria endemic western Kenya, variations in climatic factors played a key role in driving malaria incidence during 2008-2019, despite high bed net coverage and use among the population. The model captures some of the main mechanisms of human, parasite, and vector dynamics, and opens the possibility to forecast malaria in endemic regions, taking into account the interaction between future climatic conditions and intervention scenarios.

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

Background

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

Methods

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

Results

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

Conclusions

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

Six decades of malaria vector control in southern Africa: a review of the entomological evidence-base

BACKGROUND

Countries in the southern Africa region have set targets for malaria elimination between 2020 and 2030. Malaria vector control is among the key strategies being implemented to achieve this goal. This paper critically reviews published entomological research over the past six decades in three frontline malaria elimination countries namely, Botswana Eswatini and Namibia, and three second-line malaria elimination countries including Mozambique, Zambia, and Zimbabwe. The objective of the review is to assess the current knowledge and highlight gaps that need further research attention to strengthen evidence-based decision-making toward malaria elimination.

METHODS

Publications were searched on the PubMed engine using search terms: "(malaria vector control OR vector control OR malaria vector*) AND (Botswana OR Swaziland OR Eswatini OR Zambia OR Zimbabwe OR Mozambique)". Opinions, perspectives, reports, commentaries, retrospective analysis on secondary data protocols, policy briefs, and reviews were excluded.

RESULTS

The search resulted in 718 publications with 145 eligible and included in this review for the six countries generated over six decades. The majority (139) were from three countries, namely Zambia (59) and Mozambique (48), and Zimbabwe (32) whilst scientific publications were relatively scanty from front-line malaria elimination countries, such as Namibia (2), Botswana (10) and Eswatini (4). Most of the research reported in the publications focused on vector bionomics generated mostly from Mozambique and Zambia, while information on insecticide resistance was mostly available from Mozambique. Extreme gaps were identified in reporting the impact of vector control interventions, both on vectors and disease outcomes. The literature is particularly scanty on important issues such as change of vector ecology over time and space, intervention costs, and uptake of control interventions as well as insecticide resistance.

CONCLUSIONS

The review reveals a dearth of information about malaria vectors and their control, most noticeable among the frontline elimination countries: Namibia, Eswatini and Botswana. It is of paramount importance that malaria vector research capacity and routine entomological monitoring and evaluation are strengthened to enhance decision-making, considering changing vector bionomics and insecticide resistance, among other determinants of malaria vector control.

Forecasting Malaria Morbidity to 2036 Based on Geo-Climatic Factors in the Democratic Republic of Congo

BACKGROUND

Malaria is a global burden in terms of morbidity and mortality. In the Democratic Republic of Congo, malaria prevalence is increasing due to strong climatic variations. Reductions in malaria morbidity and mortality, the fight against climate change, good health and well-being constitute key development aims as set by the United Nations Sustainable Development Goals (SDGs). This study aims to predict malaria morbidity to 2036 in relation to climate variations between 2001 and 2019, which may serve as a basis to develop an early warning system that integrates monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns.

METHODS

Meteorological data were collected at the Mettelsat and the database of the Epidemiological Surveillance Directorate including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria, was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. Malaria prevalence and mortality rates by year and province using direct standardization and mean annual percentage change were calculated using DRC mid-year populations. Time series combining several predictive models were used to forecast malaria epidemic episodes to 2036. Finally, the impact of climatic factors on malaria morbidity was modeled using multivariate time series analysis.

RESULTS

The geographical distribution of malaria prevalence from 2001 and 2019 shows strong disparities between provinces with the highest of 7700 cases per 100,000 people at risk for South Kivu. In the northwest, malaria prevalence ranges from 4980 to 7700 cases per 100,000 people at risk. Malaria has been most deadly in Sankuru with a case-fatality rate of 0.526%, followed by Kasai (0.430%), Kwango (0.415%), Bas-Uélé, (0.366%) and Kwilu (0.346%), respectively. However, the stochastic trend model predicts an average annual increase of 6024.07 malaria cases per facility with exponential growth in epidemic waves over the next 200 months of the study. This represents an increase of 99.2%. There was overwhelming evidence of associations between geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at two meters altitude and malaria morbidity (p < 0.0001).

CONCLUSIONS

The stochastic trends in our time series observed in this study suggest an exponential increase in epidemic waves over the next 200 months of the study. The increase in new malaria cases is statistically related to population density, average number of rainy days, average wind speed, and unstable and intermediate epidemiological facies. Therefore, the results of this research should provide relevant information for the Congolese government to respond to malaria in real time by setting up a warning system integrating the monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns.

Distribution and Risk Factors of Malaria in the Greater Accra Region in Ghana

Malaria remains a serious public health challenge in Ghana including the Greater Accra Region. This study aimed to quantify the spatial, temporal and spatio-temporal patterns of malaria in the Greater Accra Region to inform targeted allocation of health resources. Malaria cases data from 2015 to 2019 were obtained from the Ghanaian District Health Information and Management System and aggregated at a district and monthly level. Spatial analysis was conducted using the Global Moran's I, Getis-Ord Gi*, and local indicators of spatial autocorrelation. Kulldorff's space-time scan statistics were used to investigate space-time clustering. A negative binomial regression was used to find correlations between climatic factors and sociodemographic characteristics and the incidence of malaria. A total of 1,105,370 malaria cases were reported between 2015 and 2019. Significant seasonal variation was observed, with June and July being the peak months of reported malaria cases. The hotspots districts were Kpone-Katamanso Municipal District, Ashaiman Municipal Districts, Tema Municipal District, and La-Nkwantanang-Madina Municipal District. While La-Nkwantanang-Madina Municipal District was high-high cluster. The Spatio-temporal clusters occurred between February 2015 and July 2017 in the districts of Ningo-Prampram, Shai-Osudoku, Ashaiman Municipal, and Kpone-Katamanso Municipal with a radius of 26.63 km and an relative risk of 4.66 (p < 0.001). Malaria cases were positively associated with monthly rainfall (adjusted odds ratio [AOR] = 1.01; 95% confidence interval [CI] = 1.005, 1.016) and the previous month's cases (AOR = 1.064; 95% CI 1.062, 1.065) and negatively correlated with minimum temperature (AOR = 0.86, 95% CI = 0.823, 0.899) and population density (AOR = 0.996, 95% CI = 0.994, 0.998). Malaria control and prevention should be strengthened in hotspot districts in the appropriate months to improve program effectiveness.

Malaria epidemic and transmission foci in highland of Kisii, western Kenya

Background

The vulnerable population within the malaria epidemic zone remains at risk of increased burden and fatality. This is because of unpreparedness and overstretching of healthcare capacity in the event of a full-fledged epidemic. The purpose of this study was to determine the prevalence of microscopic and submicroscopic infections, as well as map specific Plasmodium transmission foci, in the malaria epidemic-prone zone of Kisii highland.

Methodology

Patients seeking malaria treatment at Eramba health facility in the epidemic-prone zone of Kisii highland were enrolled in the study. Malaria outpatient data for the entire month of May were also included in the analysis. Patients' finger prick blood smears were examined for microscopic infections, while a real-time polymerase chain reaction targeting the Plasmodium species 18S rRNA gene was used to detect the presence of submicroscopic infections on DNA extracted from dry blood spots.

Results

Based on outpatient data, the malaria positivity rate was 20.7% (231/1115, 95% CI, 0.18–0.23). The positivity rate varied significantly by age group (χ² = 75.05, df 2, p < 0.0001). Children under the age of five had the highest positivity rate (27.8%, 78/281), followed by children aged 5–15 years (19.4%, 69/356), and individuals aged 15 years and above (17.6%, 84/478). Out of the 102 patients recruited, the positivity rate by microscopy was 57.8% (59/102) and 72.5% (74/102) by RT-PCR. Most of the microscopic infections (40.7%, 24/59) were from Morara and Nyabikondo villages in Rioma and Kiomooncha sublocations, respectively. The submicroscopic prevalence was 14.7% (15/102) and was observed only in patients from high-infection villages in Rioma (15.8%, 9/57) and Kiomooncha (16.2%, 6/37) sublocations. Across gender and age groups, females (19.7%, 12/61) and patients aged 15 years and above (21.1%, 8/38) had high levels of submicroscopic infections. There were two mixed infections of P. falciparum/P. malariae and P. falciparum/P. ovale, both from patients residing in Kiomooncha sublocation.

Conclusion

Plasmodium falciparum infections remained relatively high in the Marani subcounty. Infections were concentrated in two villages, which could serve as a target for future public health intervention, particularly during a malaria epidemic.

Exploring predictive frameworks for malaria in Burundi

In Burundi, malaria infection has been increasing in the last decade despite efforts to increase access to health services, and several intervention programs. The use of heterogeneous data can help to build predictive models of malaria cases. We built predictive frameworks: the generalized linear model (GLM), and artificial neural network (ANN), to predict malaria cases in four sub-groups and the overall general population. Descriptive results showed that more than half of malaria infections are observed in pregnant women and children under 5 years, with high burden to children between 12 and 59 months. Modelling results showed that, ANN model performed better in predicting total cases compared to GLM. Both model frameworks showed that education rates and Insecticide Treated Bed Nets (ITNs) had decreasing effects on malaria cases, some other variables had an increasing effect. Thus, malaria control and prevention interventions program are encouraged to understand those variables, and take appropriate measures such as providing ITNs, sensitization in schools and the communities, starting within high dense communities, among others. Early prediction of cases can provide timely information needed to be proactive for intervention strategies, and it can help to mitigate the epidemics and reduce its impact on populations and the economy.

Risk associations of submicroscopic malaria infection in lakeshore, plateau and highland areas of Kisumu County in western Kenya

BACKGROUND

Persons with submicroscopic malaria infection are a major reservoir of gametocytes that sustain malaria transmission in sub-Saharan Africa. Despite recent decreases in the national malaria burden in Kenya due to vector control interventions, malaria transmission continues to be high in western regions of the country bordering Lake Victoria. The objective of this study was to advance knowledge of the topographical, demographic and behavioral risk factors associated with submicroscopic malaria infection in the Lake Victoria basin in Kisumu County.

METHODS

Cross-sectional community surveys for malaria infection were undertaken in three eco-epidemiologically distinct zones in Nyakach sub-County, Kisumu. Adjacent regions were topologically characterized as lakeshore, hillside and highland plateau. Surveys were conducted during the 2019 and 2020 wet and dry seasons. Finger prick blood smears and dry blood spots (DBS) on filter paper were collected from 1,777 healthy volunteers for microscopic inspection and real time-PCR (RT-PCR) diagnosis of Plasmodium infection. Persons who were PCR positive but blood smear negative were considered to harbor submicroscopic infections. Topographical, demographic and behavioral risk factors were correlated with community prevalence of submicroscopic infections.

RESULTS

Out of a total of 1,777 blood samples collected, 14.2% (253/1,777) were diagnosed as submicroscopic infections. Blood smear microscopy and RT-PCR, respectively, detected 3.7% (66/1,777) and 18% (319/1,777) infections. Blood smears results were exclusively positive for P. falciparum, whereas RT-PCR also detected P. malariae and P. ovale mono- and co-infections. Submicroscopic infection prevalence was associated with topographical variation (χ2 = 39.344, df = 2, p<0.0001). The highest prevalence was observed in the lakeshore zone (20.6%, n = 622) followed by the hillside (13.6%, n = 595) and highland plateau zones (7.9%, n = 560). Infection prevalence varied significantly according to season (χ2 = 17.374, df = 3, p<0.0001). The highest prevalence was observed in residents of the lakeshore zone in the 2019 dry season (29.9%, n = 167) and 2020 and 2019 rainy seasons (21.5%, n = 144 and 18.1%, n = 155, respectively). In both the rainy and dry seasons the likelihood of submicroscopic infection was higher in the lakeshore (AOR: 2.71, 95% CI = 1.85-3.95; p<0.0001) and hillside (AOR: 1.74, 95% CI = 1.17-2.61, p = 0.007) than in the highland plateau zones. Residence in the lakeshore zone (p<0.0001), male sex (p = 0.025), school age (p = 0.002), and living in mud houses (p = 0.044) increased the risk of submicroscopic malaria infection. Bed net use (p = 0.112) and occupation (p = 0.116) were not associated with submicroscopic infection prevalence.

CONCLUSION

Topographic features of the local landscape and seasonality are major correlates of submicroscopic malaria infection in the Lake Victoria area of western Kenya. Diagnostic tests more sensitive than blood smear microscopy will allow for monitoring and targeting geographic sites where additional vector interventions are needed to reduce malaria transmission.

Bayesian spatial modelling of geostatistical data using INLA and SPDE methods: A case study predicting malaria risk in Mozambique

Bayesian spatial models are widely used to analyse data that arise in scientific disciplines such as health, ecology, and the environment. Traditionally, Markov chain Monte Carlo (MCMC) methods have been used to fit these type of models. However, these are highly computationally intensive methods that present a wide range of issues in terms of convergence and can become infeasible in big data problems. The integrated nested Laplace approximation (INLA) method is a computational less-intensive alternative to MCMC that allows us to perform approximate Bayesian inference in latent Gaussian models such as generalised linear mixed models and spatial and spatio-temporal models. This approach can be used in combination with the stochastic partial differential equation (SPDE) approach to analyse geostatistical data that have been collected at particular sites to predict the spatial process underlying the data as well as to assess the effect of covariates and model other sources of variability. Here we demonstrate how to fit a Bayesian spatial model using the INLA and SPDE approaches applied to freely available data of malaria prevalence and risk factors in Mozambique. We show how to fit and interpret the model to predict malaria risk and assess the effect of covariates using the R-INLA package, and provide the R code necessary to reproduce the results or to use it in other spatial applications.

Phytochemical and Antimalarial Effects of Ecballium elaterium (L.) Rich. Growing in Iran

Background: Malaria is a well-recognized parasitic disease and a serious public health problem worldwide, particularly in tropical and subtropical areas. Objectives: This study was conducted to investigate the antimalarial properties of extracts with different polarities from the various parts of Ecballium elaterium (L.) Rich. (or wild cucumber) as a perennial herbaceous plant growing in Gilan and Azerbaijan provinces of Iran. Methods: The air-dried and powdered fruits, seeds, and roots of E. elaterium were extracted using three solvents with different polarities, n-Hexane (n-Hex), dichloromethane (DCM), and methanol (MeOH). The MeOH extract of roots was subjected to fractionalizing by a C18 Sep-Pak cartridge. All extracts and fractions with different polarities were assessed for their antimalarial activity using the cell-free beta-hematin formation test, and the structural groups of the fractions were identified by Thin-Layer Chromatography (TLC). Results: According to our results, the MeOH extracts of the plant’s roots presented considerable antimalarial effects with an IC50 value of 0.124 ± 0.0002 mg/mL. Bioactivity-guided fractionation of root MeOH extract by solid phase extraction (SPE) afforded six fractions. The 20% fraction showed the most potent antimalarial effect with an IC50 value of 0.167 ± 0.002 mg/mL. Moreover, the three fractions of 80%, 60%, and 100% methanol/water demonstrated considerable antimalarial activities. Phytochemical analysis of potent fractions of E. elaterium suggested the presence of flavonoids in 20% and 60% fractions and flavonoids and triterpenoids in 80% and 100% fractions. Conclusions: According to our primary phytochemical investigation on the six SPE fractions, it is recommended to purify the active constituents of the most effective fractions and investigate their biological effects in animal models.

Climate Change and Health Preparedness in Africa: Analysing Trends in Six African Countries

Climate change is a global problem, which affects the various geographical regions at different levels. It is also associated with a wide range of human health problems, which pose a burden to health systems, especially in regions such as Africa. Indeed, across the African continent public health systems are under severe pressure, partly due to their fragile socioeconomic conditions. This paper reports on a cross-sectional study in six African countries (Ghana, Nigeria, South Africa, Namibia, Ethiopia, and Kenya) aimed at assessing their vulnerabilities to climate change, focusing on its impacts on human health. The study evaluated the levels of information, knowledge, and perceptions of public health professionals. It also examined the health systems’ preparedness to cope with these health hazards, the available resources, and those needed to build resilience to the country’s vulnerable population, as perceived by health professionals. The results revealed that 63.1% of the total respondents reported that climate change had been extensively experienced in the past years, while 32% claimed that the sampled countries had experienced them to some extent. Nigerian respondents recorded the highest levels (67.7%), followed by Kenya with 66.6%. South Africa had the lowest level of impact as perceived by the respondents (50.0%) when compared with the other sampled countries. All respondents from Ghana and Namibia reported that health problems caused by climate change are common in the two countries. As perceived by the health professionals, the inadequate resources reiterate the need for infrastructural resources, medical equipment, emergency response resources, and technical support. The study’s recommendations include the need to improve current policies at all levels (i.e., national, regional, and local) on climate change and public health and to strengthen health professionals’ skills. Improving the basic knowledge of health institutions to better respond to a changing climate is also recommended. The study provides valuable insights which may be helpful to other nations in Sub-Saharan Africa.

Effects of rainfall, temperature and topography on malaria incidence in elimination targeted district of Ethiopia

BACKGROUND

Climate and environmental factors could be one of the primary factors that drive malaria transmission and it remains to challenge the malaria elimination efforts. Hence, this study was aimed to evaluate the effects of meteorological factors and topography on the incidence of malaria in the Boricha district in Sidama regional state of Ethiopia.

METHODS

Malaria morbidity data recorded from 2010 to 2017 were obtained from all public health facilities of Boricha District in the Sidama regional state of Ethiopia. The monthly malaria cases, rainfall, and temperature (minimum, maximum, and average) were used to fit the ARIMA model to compute the malaria transmission dynamics and also to forecast future incidence. The effects of the meteorological variables and altitude were assessed with a negative binomial regression model using R version 4.0.0. Cross-correlation analysis was employed to compute the delayed effects of meteorological variables on malaria incidence.

RESULTS

Temperature, rainfall, and elevation were the major determinants of malaria incidence in the study area. A regression model of previous monthly rainfall at lag 0 and Lag 2, monthly mean maximum temperature at lag 2 and Lag 3, and monthly mean minimum temperature at lag 3 were found as the best prediction model for monthly malaria incidence. Malaria cases at 1801-1900 m above sea level were 1.48 times more likely to occur than elevation ≥ 2000 m.

CONCLUSIONS

Meteorological factors and altitude were the major drivers of malaria incidence in the study area. Thus, evidence-based interventions tailored to each determinant are required to achieve the malaria elimination target of the country.

Disparities in Risks of Malaria Associated with Climatic Variability among Women, Children and Elderly in the Chittagong Hill Tracts of Bangladesh

Malaria occurrence in the Chittagong Hill Tracts in Bangladesh varies by season and year, but this pattern is not well characterized. The role of environmental conditions on the occurrence of this vector-borne parasitic disease in the region is not fully understood. We extracted information on malaria patients recorded in the Upazila (sub-district) Health Complex patient registers of Rajasthali in Rangamati district of Bangladesh from February 2000 to November 2009. Weather data for the study area and period were obtained from the Bangladesh Meteorological Department. Non-linear and delayed effects of meteorological drivers, including temperature, relative humidity, and rainfall on the incidence of malaria, were investigated. We observed significant positive association between temperature and rainfall and malaria occurrence, revealing two peaks at 19 °C (logarithms of relative risks (logRR) = 4.3, 95% CI: 1.1-7.5) and 24.5 °C (logRR = 4.7, 95% CI: 1.8-7.6) for temperature and at 86 mm (logRR = 19.5, 95% CI: 11.7-27.3) and 284 mm (logRR = 17.6, 95% CI: 9.9-25.2) for rainfall. In sub-group analysis, women were at a much higher risk of developing malaria at increased temperatures. People over 50 years and children under 15 years were more susceptible to malaria at increased rainfall. The observed associations have policy implications. Further research is needed to expand these findings and direct resources to the vulnerable populations for malaria prevention and control in the Chittagong Hill Tracts of Bangladesh and the region with similar settings.

The impact of climate change on mosquito-borne diseases in Africa

Despite being one of the continents with the least greenhouse gas emissions, no continent is being struck as severely by climate change (CC) as Africa. Mosquito-borne diseases (MBD) cause major human diseases in this continent. Current knowledge suggests that MBD range could expand dramatically in response to CC. This study aimed at assessing the relationship between CC and MBD in Africa. Methods For this purpose, a systematic peer review was carried out, considering all articles indexed in PubMed, Scopus, Embase and CENTRAL. Search terms referring to MBD, CC and environmental factors were screened in title, abstract and keywords.Results A total of twenty-nine studies were included, most of them on malaria (61%), being Anopheles spp. (61%) the most commonly analyzed vector, mainly in Eastern Africa (48%). Seventy-nine percent of these studies were based on predictive models. Seventy-two percent of the reviewed studies considered that CC impacts on MBD epidemiology. MBD prevalence will increase according to 69% of the studies while 17% predicted a decrease. MBD expansion throughout the continent was also predicted. Most studies showed a positive relationship between observed or predicted results and CC. However, there was a great heterogeneity in methodologies and a tendency to reductionism, not integrating other variables that interact with both the environment and MBD. In addition, most results have not yet been tested. A global health approach is desirable in this kind of research. Nevertheless, we cannot wait for science to approve something that needs to be addressed now to avoid greater effects in the future.

Malaria patterns across altitudinal zones of Mount Elgon following intensified control and prevention programs in Uganda

Background

Malaria remains a major tropical vector-borne disease of immense public health concern owing to its debilitating effects in sub-Saharan Africa. Over the past 30 years, the high altitude areas in Eastern Africa have been reported to experience increased cases of malaria. Governments including that of the Republic of Uganda have responded through intensifying programs that can potentially minimize malaria transmission while reducing associated fatalities. However, malaria patterns following these intensified control and prevention interventions in the changing climate remains widely unexplored in East African highland regions. This study thus analyzed malaria patterns across altitudinal zones of Mount Elgon, Uganda.

Methods

Times-series data on malaria cases (2011–2017) from five level III local health centers occurring across three altitudinal zones; low, mid and high altitude was utilized. Inverse Distance Weighted (IDW) interpolation regression and Mann Kendall trend test were used to analyze malaria patterns. Vegetation attributes from the three altitudinal zones were analyzed using Normalized Difference Vegetation Index (NDVI) was used to determine the Autoregressive Integrated Moving Average (ARIMA) model was used to project malaria patterns for a 7 year period.

Results

Malaria across the three zones declined over the study period. The hotspots for malaria were highly variable over time in all the three zones. Rainfall played a significant role in influencing malaria burdens across the three zones. Vegetation had a significant influence on malaria in the higher altitudes. Meanwhile, in the lower altitude, human population had a significant positive correlation with malaria cases.

Conclusions

Despite observed decline in malaria cases across the three altitudinal zones, the high altitude zone became a malaria hotspot as cases variably occurred in the zone. Rainfall played the biggest role in malaria trends. Human population appeared to influence malaria incidences in the low altitude areas partly due to population concentration in this zone. Malaria control interventions ought to be strengthened and strategically designed to achieve no malaria cases across all the altitudinal zones. Integration of climate information within malaria interventions can also strengthen eradication strategies of malaria in such differentiated altitudinal zones.

Drought and Illness among Young Children in Uganda, 2009-2012

Changing precipitation patterns resulting from climate change are likely to have deleterious effects on health. We examined historical relationships between precipitation and diarrhea, cough, and fever among children aged 0-24 months in Uganda, a drought-prone region. Using data from the Uganda National Panel Survey from 2009 to 2012 (2,324 observations), we specified logistic regression models evaluating the relationships between deviations from annual and 30-day precipitation and caregiver-reported diarrhea, cough, and fever, adjusting for sociodemographic characteristics and including enumeration of area-fixed effects. Nonlinearities were assessed using restricted cubic splines. We observed nonlinear (J-shaped) relationships between deviations from annual precipitation and the three child illness outcomes. These J-shaped relationships represented steep reductions in illness with increasing precipitation at lower levels of rainfall and a leveling off at higher levels, with a small increase at higher levels. We did not find evidence for a relationship between 30-day precipitation and childhood illness. Trends of reduced rainfall in Uganda are likely having negative effects on child health.

Malaria Outbreak Facilitated by Appearance of Vector-Breeding Sites after Heavy Rainfall and Inadequate Preventive Measures: Nwoya District, Northern Uganda, February-May 2018

Background

Malaria is a leading cause of morbidity and mortality in Uganda. In April 2018, malaria cases surged in Nwoya District, Northern Uganda, exceeding expected limits and thereby requiring epidemic response. We investigated this outbreak to estimate its magnitude, identify exposure factors for transmission, and recommend evidence-based control measures.

Methods

We defined a malaria case as onset of fever in a resident of Anaka subcounty, Koch Goma subcounty, and Nwoya Town Council, Nwoya District, with a positive rapid diagnostic test or microscopy for malaria from 1 February to 25 May 2018. We reviewed medical records in all health facilities of affected subcounties to find cases. In a case-control study, we compared exposure factors between case-persons and asymptomatic controls matched by age and village. We also conducted entomological assessments on vector density and behavior.

Results

We identified 3,879 case-persons (attack rate [AR] = 6.5%) and two deaths (case-fatality rate = 5.2/10,000). Females (AR = 8.1%) were more affected than males (AR = 4.7%) (p < 0.0001). Of all age groups, 5–18 years (AR = 8.4%) were most affected. Heavy rain started in early March 2018, and a propagated outbreak followed in the first week of April 2018. In the case-control study, 55% (59/107) of case-persons and 18% (19/107) of controls had stagnant water around households for several days following rainfall (OR_M-H = 5.6, 95% CI = 3.0–11); 25% (27/107) of case-persons and 51% (55/107) of controls wore full extremity covering clothes during evening hours (OR_M-H = 0.30, 95% CI = 0.20–0.60); 71% (76/107) of case-persons and 85% (91/107) of controls slept under a long-lasting insecticide-treated net (LLIN) 14 days before symptom onset (OR_M-H = 0.43, 95% CI = 0.22–0.85); 37% (40/107) of case-persons and 52% (56/107) of controls had access to at least one LLIN per 2 household members (OR_M-H = 0.54, 95% CI = 0.30–0.97). Entomological assessment indicated active breeding sites in the entire study area; Anopheles gambiae sensu lato species were the predominant vector.

Conclusion

Increased vector-breeding sites after heavy rainfall and inadequate malaria preventive measures were found to have contributed to this outbreak. We recommended increasing coverage for LLINs and larviciding breeding sites in the area.

Single-nucleotide polymorphism characterization of gametocyte development 1 gene in Plasmodium falciparum isolates from Baringo, Uasin Gishu, and Nandi Counties, Kenya

INTRODUCTION

Plasmodium falciparum relies on gametocytogenesis to transmit from humans to mosquitoes. Gametocyte development 1 (Pfgdv1) is an upstream activator and epigenetic controller of gametocytogenesis. The emergence of drug resistance is a major public health concern and this requires the development of new strategies that target the transmission of malaria. As a putative drug target, Pfgdv1 has not been characterized to identify its polymorphisms and alleles under selection and how such polymorphisms influence protein structure.

METHODS

This study characterized single-nucleotide polymorphisms (SNPs) in primary sequences (n = 30) of Pfgdv1 gene generated from thirty blood samples collected from patients infected with P. falciparum and secondary sequences (n = 216) retrieved from PlasmoDB. ChromasPro, MUSCLE, Tajima's D statistic, SLAC, and STRUM were used in editing raw sequences, performing multiple sequence alignment (MSA), identifying signatures of selection, detecting codon sites under selection pressure, and determining the effect of SNPs, respectively.

RESULTS

MSA of primary and secondary sequences established the existence of five SNPs, consisting of four non-synonymous substitutions (nsSNPs) (p.P217H, p.R398Q, p.H417N, and p.D497E), and a synonymous substitution (p.S514S). The analysis of amino acid changes reveals that p.P217H, p.R398Q, and p.H417N comprise non-conservative changes. Tajima's D statistic showed that these SNPs were under balancing selection, while SLAC analysis identified p.P217H to be under the strongest positive selection. . Further analysis based on thermodynamics indicated that p.P217H has a destabilizing effect, while p.R398Q and p.D497E have stabilizing effects on the protein structure.

CONCLUSIONS

The existence of four nsSNPs implies that Pfgdv1 has a minimal diversity in the encoded protein. Selection analysis demonstrates that these nsSNPs are under balancing selection in both local and global populations. However, p.P217H exhibits positive directional selection consistent with previous reports where it showed differentiatial selection of P. falciparum in low and high transmission regions. Therefore, in-silico prediction and experimental determination of protein structure are necessary to evaluate Pfgdv1 as a target candidate for drug design and development.

The effects of climate change on human health in Africa, a dermatologic perspective: a report from the International Society of Dermatology Climate Change Committee

Throughout much of the African continent, healthcare systems are already strained in their efforts to meet the needs of a growing population using limited resources. Climate change threatens to undermine many of the public health gains that have been made in this region in the last several decades via multiple mechanisms, including malnutrition secondary to drought-induced food insecurity, mass human displacement from newly uninhabitable areas, exacerbation of environmentally sensitive chronic diseases, and enhanced viability of pathogenic microbes and their vectors. We reviewed the literature describing the various direct and indirect effects of climate change on diseases with cutaneous manifestations in Africa. We included non-communicable diseases such as malignancies (non-melanoma skin cancers), inflammatory dermatoses (i.e. photosensitive dermatoses, atopic dermatitis), and trauma (skin injury), as well as communicable diseases and neglected tropical diseases. Physicians should be aware of the ways in which climate change threatens human health in low- and middle-income countries in general, and particularly in countries throughout Africa, the world's lowest-income and second most populous continent.

Malaria and the climate in Karachi: An eight year review

Background and Objective:

Malaria is an arthropod-borne infectious disease transmitted by the mosquito Anopheles and claims millions of lives globally every year. Reasons for failure to eradicate this disease are multifactorial. The seasonality of the malaria is principally determined by climatic factors conducive for breeding of the vector. We aimed to study the relationship between climatic variability and the seasonality of malaria over an eight-year duration.

Methods:

This was a retrospective medical chart review of 8,844 confirmed cases of malaria which presented to The Indus Hospital, Karachi from January 2008 to November 2015. Cases were plotted against meteorological data for Karachi to elicit monthly variation.

Results:

A secular incline and seasonality in malaria cases over the duration of eight years was seen. More cases were reported in the summer, rainy season compared with the other three seasons in each year. There was significant association with specific climate variables such as temperature, moisture, and humidity.

Conclusion:

There is a marked seasonal variation of malaria in Karachi, influenced by various environmental factors. Identification of the ‘the concentrated period’ of malaria can be helpful for policymakers to deploy malaria control interventions.

Impact of past and on-going changes on climate and weather on vector-borne diseases transmission: a look at the evidence

Background

The climate variables that directly influence vector-borne diseases’ ecosystems are mainly temperature and rainfall. This is not only because the vectors bionomics are strongly dependent upon these variables, but also because most of the elements of the systems are impacted, such as the host behavior and development and the pathogen amplification. The impact of the climate changes on the transmission patterns of these diseases is not easily understood, since many confounding factors are acting together. Consequently, knowledge of these impacts is often based on hypothesis derived from mathematical models. Nevertheless, some direct evidences can be found for several vector-borne diseases.

Main body

Evidences of the impact of climate change are available for malaria, arbovirus diseases such as dengue, and many other parasitic and viral diseases such as Rift Valley Fever, Japanese encephalitis, human African trypanosomiasis and leishmaniasis. The effect of temperature and rainfall change as well as extreme events, were found to be the main cause for outbreaks and are alarming the global community. Among the main driving factors, climate strongly influences the geographical distribution of insect vectors, which is rapidly changing due to climate change. Further, in both models and direct evidences, climate change is seen to be affecting vector-borne diseases more strikingly in fringe of different climatic areas often in the border of transmission zones, which were once free of these diseases with human populations less immune and more receptive. The impact of climate change is also more devastating because of the unpreparedness of Public Health systems to provide adequate response to the events, even when climatic warning is available. Although evidences are strong at the regional and local levels, the studies on impact of climate change on vector-borne diseases and health are producing contradictory results at the global level.

Conclusions

In this paper we discuss the current state of the results and draw on evidences from malaria, dengue and other vector-borne diseases to illustrate the state of current thinking and outline the need for further research to inform our predictions and response.

Electronic supplementary material

The online version of this article (10.1186/s40249-019-0565-1) contains supplementary material, which is available to authorized users.

Larval species diversity, seasonal occurrence and larval habitat preference of mosquitoes transmitting Rift Valley fever and malaria in Baringo County, Kenya

Background

Baseline information that is essential for determining the areas to target with larval control includes estimates of vector diversity and larval habitat preferences. Due to a lack of such information in Baringo County, Kenya, this study assessed species diversity and larval habitat preference of potential mosquito vectors of Rift Valley fever (RVF) and malaria.

Methods

Mosquito larvae were sampled from nine types of larval habitats and were identified morphologically. Species diversity was estimated by the Shannon’s diversity index while larval habitat preference by RVF and malaria vectors was determined by ANOVA.

Results

A total of 7724 immature mosquitoes comprising 17 species belonging to four genera, namely Anopheles, Culex, Aedes and Mansonia, were identified. Among the 17 species, three Anopheles species are responsible for malaria transmission: An. gambiae (s.l.), An. funestus (s.l.) and An. pharoensis. Rift Valley fever vectors included Mansonia spp. and Culex spp. The highest Shannon's diversity index was observed during the cold dry season (H = 2.487) and in the highland zone (H = 2.539) while the lowest diversity was recorded during the long rain season (H = 2.354) and in the riverine zone (H = 2.085). Ditches had the highest mean number of Anopheles larvae (16.6 larvae per sample) followed by swamp (12.4) and seasonal riverbed (10.7). Water pit and water pan had low mean numbers of Anopheles larvae (1.4 and 1.8, respectively) but relatively high mean numbers of culicines (16.9 and 13.7, respectively). Concrete tank was the least sampled type of habitat but had highest mean number of culicine larvae (333.7 l) followed distantly by water spring (38.9) and swamp (23.5). Overall, larval habitats were significantly different in terms of larval density (F_(8,334) = 2.090, P = 0.036).

Conclusions

To our knowledge, the present study reports culicine larval species diversity in Baringo for the first time and the most preferred habitats were concrete tanks, water springs and swamps. Habitats preferred by Anopheles were mainly riverbed pools, ditches and swamps. Environmental management targeting the habitats most preferred by potential vectors can be part of integrated vector control in Baringo, especially during dry seasons.

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.

Investigating the upsurge of malaria prevalence in Zambia between 2010 and 2015: a decomposition of determinants

Background

Malaria is among the top causes of mortality and morbidity in Zambia. Efforts to control, prevent, and eliminate it have been intensified in the past two decades which has contributed to reductions in malaria prevalence and under-five mortality. However, there was a 21% upsurge in malaria prevalence between 2010 and 2015. Zambia is one of the only 13 countries to record an increase in malaria among 91 countries monitored by the World Health Organization in 2015. This study investigated the upsurge by decomposition of drivers of malaria.

Methods

The study used secondary data from three waves of nationally representative cross-sectional surveys on key malaria indicators conducted in 2010, 2012 and 2015. Using multivariable logistic regression, determinants of malaria prevalence were identified and then marginal effects of each determinant were derived. The marginal effects were then combined with changes in coverage rates of determinants between 2010 and 2015 to obtain the magnitude of how much each variable contributed to the change in the malaria prevalence.

Results

The odds ratio of malaria for those who slept under an insecticide-treated net (ITN) was 0.90 (95% CI 0.77–0.97), indoor residual spraying (IRS) was 0.66 (95% CI 0.49–0.89), urban residence was 0.23 (95% CI 0.15–0.37), standard house was 0.40 (95% CI 0.35–0.71) and age group 12–59 Months against those below 12 months was 4.04 (95% CI 2.80–5.81). Decomposition of prevalence changes by determinants showed that IRS reduced malaria prevalence by − 0.3% and ITNs by − 0.2% however, these reductions were overridden by increases in prevalence due to increases in the proportion of more at-risk children aged 12–59 months by + 2.3% and rural residents by + 2.2%.

Conclusion

The increases in interventions, such as ITNs and IRS, were shown to have contributed to malaria reduction in 2015; however, changes in demographics such as increases in the proportion of more at risk groups among under-five children and rural residents may have overridden the impact of these interventions and resulted in an overall increase. The upsurge in malaria in 2015 compared to 2010 may not have been due to weaknesses in programme interventions but due to increases in more at-risk children and rural residents compared to 2010. The apparent increase in rural residents in the sample population may not have been a true reflection of the population structure but due to oversampling in rural areas which was not fully adjusted for. The increase in malaria prevalence may therefore have been overestimated.

The effects of socioecological factors on variation of communicable diseases: A multiple-disease study at the national scale of Vietnam

OBJECTIVE

To examine the effects of socioecological factors on multiple communicable diseases across Vietnam.

METHODS

We used the Moran's I tests to evaluate spatial clusters of diseases and applied multilevel negative binomial regression models using the Bayesian framework to analyse the association between socioecological factors and the diseases queried by oral, airborne, vector-borne, and animal transmission diseases.

RESULTS AND SIGNIFICANCE

The study found that oral-transmission diseases were spatially distributed across the country; whereas, the airborne-transmission diseases were more clustered in the Northwest and vector-borne transmission diseases were more clustered in the South. Most of diseases were sensitive with climatic factors. For instance, a 1°C increase in average temperature is significantly associated with 0.4% (95CI, 0.3-0.5), 2.5% (95%CI, 1.4-3.6), 0.9% (95%CI, 0.6-1.4), 1.1% (95%CI), 5% (95%CI, 3-.7.4), 0.4% (95%CI, 0.2-0.7), and 2% (95%CI, 1.5-2.8) increase in risk of diarrhoea, shigellosis, mumps, influenza, dengue, malaria, and rabies respectively. The influences of socio-economic factors on risk of communicable diseases are varied by factors with the biggest influence of population density. The research findings reflect an important implication for the climate change adaptation strategies of health sectors. A development of weather-based early warning systems should be considered to strengthen communicable disease prevention in Vietnam.

The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

•

A LASSO based forecast model for endemic infectious diseases is proposed.

•

Predictions at 4 weeks achieve desirable accuracy.

•

Models predict outbreaks but may struggle to predict outbreak size.

Introduction

Accurate and timely prediction for endemic infectious diseases is vital for public health agencies to plan and carry out any control methods at an early stage of disease outbreaks. Climatic variables has been identified as important predictors in models for infectious disease forecasts. Various approaches have been proposed in the literature to produce accurate and timely predictions and potentially improve public health response.

Methods

We assessed how the machine learning LASSO method may be useful in providing useful forecasts for different pathogens in countries with different climates. Separate LASSO models were constructed for different disease/country/forecast window with different model complexity by including different sets of predictors to assess the importance of different predictors under various conditions.

Results

There was a more apparent cyclicity for both climatic variables and incidence in regions further away from the equator. For most diseases, predictions made beyond 4 weeks ahead were increasingly discrepant from the actual scenario. Prediction models were more accurate in capturing the outbreak but less sensitive to predict the outbreak size. In different situations, climatic variables have different levels of importance in prediction accuracy.

Conclusions

For LASSO models used for prediction, including different sets of predictors has varying effect in different situations. Short term predictions generally perform better than longer term predictions, suggesting public health agencies may need the capacity to respond at short-notice to early warnings.

Support for research towards understanding the population health vulnerabilities to vector-borne diseases: increasing resilience under climate change conditions in Africa

Background

Diseases transmitted to humans by vectors account for 17% of all infectious diseases and remain significant public health problems. Through the years, great strides have been taken towards combatting vector-borne diseases (VBDs), most notably through large scale and coordinated control programmes, which have contributed to the decline of the global mortality attributed to VBDs. However, with environmental changes, including climate change, the impact on VBDs is anticipated to be significant, in terms of VBD-related hazards, vulnerabilities and exposure. While there is growing awareness on the vulnerability of the African continent to VBDs in the context of climate change, there is still a paucity of research being undertaken in this area, and impeding the formulation of evidence-based health policy change.

Main body

One way in which the gap in knowledge and evidence can be filled is for donor institutions to support research in this area. The collaboration between the WHO Special Programme for Research and Training in Tropical Diseases (TDR) and the International Centre for Research and Development (IDRC) builds on more than 10 years of partnership in research capacity-building in the field of tropical diseases. From this partnership was born yet another research initiative on VBDs and the impact of climate change in the Sahel and sub-Saharan Africa. This paper lists the projects supported under this research initiative and provides a brief on some of the policy and good practice recommendations emerging from the ongoing implementation of the research projects.

Conclusion

Data generated from the research initiative are expected to be uptaken by stakeholders (including communities, policy makers, public health practitioners and other relevant partners) to contribute to a better understanding of the impacts of social, environmental and climate change on VBDs(i.e. the nature of the hazard, vulnerabilities, exposure), and improve the ability of African countries to adapt to and reduce the effects of these changes in ways that benefit their most vulnerable populations.

Electronic supplementary material

The online version of this article (10.1186/s40249-017-0378-z) contains supplementary material, which is available to authorized users.