Assessment of Climate-Driven Variations in Malaria Transmission in Senegal Using the VECTRI Model

Climate change and resilience of the Senegalese health system in the face of the floods in Keur Massar

This article is based on the observation that the affected populations perceive existing community-based adaptation strategies to the health effects of floods differently. We explore the resilience of the local health system to climate change (CC) in Keur Massar (Senegal) using a monographic approach based on a qualitative survey of flooded households, health professionals, hygiene agents, community health actors, administrative and local authorities, agents from the Ministries of Health and Environment, and experts from the ecological and meteorological monitoring centre (n = 72). The effects of CC on health are modulated by financial, organisational, social and cultural factors. The effects of CC on health are modulated by traditionally praised by self-centred health governance, which is often based on standardisation of problems and thus not sufficiently attuned to local contexts, especially the climate vulnerability index (CVI) of households and health structures. Despite the existence of programs to combat the consequences of CC, the notorious lack of exhaustive mapping of areas with a high CVI hinders the effective management of the health of the affected populations. A typology of forms of mobility in the context of flooding-ground floor to the upper floor, borrowing a room, renting a flat, seasonal residence-reveals inequalities in access to care as well as specific health needs management of vector-borne diseases, discontinuity of maternal, newborn and child health care, and psychosocial assistance. The article outlines how a health territorialisation based on surveillance and response mechanisms can be co-constructed and made sustainable in areas with a high CVI. Integrating this approach into national health policies allows for equity in health systems efficiently and sustainably.

Self-reported high-risk behavior among first-time and repeat replacement blood donors; a four-year retrospective study of patterns

BACKGROUND

There is no replacement for blood, and patients requiring transfusion depend on human donors, most of whom are family donors. Family donors may deny engagement in high-risk activities, which threaten the safety of donated blood. This study determined frequency of self-reported high-risk behaviors among replacement donors.

METHODS

This retrospective study recruited 1317 donor records from 2017-2020, at Mankranso Hospital, Ghana. Data from archived donor questionnaires were extracted and analyzed with SPSS and GraphPad. Frequencies, associations, and quartiles were presented.

RESULTS

The donors were predominantly males (84.4%), 17-26 years old (43.7%), informal workers (71.8%), rural inhabitants (56.5%), first-time (65.0%), and screened in the rainy season (56.3%). Donation frequency was significantly associated with age, sex, occupation, and residence. Repeat donors were significantly older (p≤0.001). More males than females were deferred (p = 0.008), drug addicts (p = 0.001), had body modifications (p = 0.025), multiple sexual partners (p = 0.045), and STIs (p≤0.001), whereas, more females were recently treated (p = 0.044). Weight loss (p = 0.005) and pregnancy (p = 0.026) were frequent among 17-26-year group, whereas, tuberculosis was frequent among 37-60-year group (p = 0.009). More first-time donors were unwell (p = 0.005), deferred (p≤0.001), pregnant (p = 0.002), drug addicts, had impending rigorous activity (p = 0.037), body modifications (p = 0.001), multiple sexual partners (p = 0.030), and STIs (p = 0.008). STIs were frequent in the dry season (p = 0.010). First-time donors had reduced hemoglobin (p = 0.0032), weight (p = 0.0003), and diastolic pressure (p = 0.0241).

CONCLUSION

Donation frequency was associated with age, sex, occupation, and residence, with first-time donors younger than repeat donors. Deferral from donation, drug addiction, body modification, multiple sexual partners, and STIs were frequent among males, whereas, more females received treatment. Tuberculosis was frequently reported among older adults, whereas, weight loss and pregnancy were frequent among younger individuals. More first-time donors reported being unwell, deferred, drug addiction, body modifications, multiple sexual partners, STIs, and pregnant. Hemoglobin, weight, and diastolic BP were reduced among first-time donors.

The effect of explicit convection on simulated malaria transmission across Africa

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Effects of Climate Variability on Malaria Transmission in Southern Côte d'Ivoire, West Africa

Malaria continues to be a major public health concern with a substantial burden in Africa. Even though it has been widely demonstrated that malaria transmission is climate-driven, there have been very few studies assessing the relationship between climate variables and malaria transmission in Côte d'Ivoire. We used the VECTRI model to predict malaria transmission in southern Côte d'Ivoire. First, we tested the suitability of VECTRI in modeling malaria transmission using ERA5 temperature data and ARC2 rainfall data. We then used the projected climatic data pertaining to 2030, 2050, and 2080 from a set of 14 simulations from the CORDEX-Africa database to compute VECTRI outputs. The entomological inoculation rate (EIR) from the VECTRI model was well correlated with the observed malaria cases from 2010 to 2019, including the peaks of malaria cases and the EIR. However, the correlation between the two parameters was not statistically significant. The VECTRI model predicted an increase in malaria transmissions in both scenarios (RCP8.5 and RCP4.5) for the time period 2030 to 2080. The monthly EIR for RCP8.5 was very high (1.74 to 1131.71 bites/person) compared to RCP4.5 (0.48 to 908 bites/person). These findings call for greater efforts to control malaria that take into account the impact of climatic factors.

Bias-Corrected CMIP5 Projections for Climate Change and Assessments of Impact on Malaria in Senegal under the VECTRI Model

On the climate-health issue, studies have already attempted to understand the influence of climate change on the transmission of malaria. Extreme weather events such as floods, droughts, or heat waves can alter the course and distribution of malaria. This study aims to understand the impact of future climate change on malaria transmission using, for the first time in Senegal, the ICTP's community-based vector-borne disease model, TRIeste (VECTRI). This biological model is a dynamic mathematical model for the study of malaria transmission that considers the impact of climate and population variability. A new approach for VECTRI input parameters was also used. A bias correction technique, the cumulative distribution function transform (CDF-t) method, was applied to climate simulations to remove systematic biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) that could alter impact predictions. Beforehand, we use reference data for validation such as CPC global unified gauge-based analysis of daily precipitation (CPC for Climate Prediction Center), ERA5-land reanalysis, Climate Hazards InfraRed Precipitation with Station data (CHIRPS), and African Rainfall Climatology 2.0 (ARC2). The results were analyzed for two CMIP5 scenarios for the different time periods: assessment: 1983-2005; near future: 2006-2028; medium term: 2030-2052; and far future: 2077-2099). The validation results show that the models reproduce the annual cycle well. Except for the IPSL-CM5B model, which gives a peak in August, all the other models (ACCESS1-3, CanESM2, CSIRO, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, inmcm4, and IPSL-CM5B) agree with the validation data on a maximum peak in September with a period of strong transmission in August-October. With spatial variation, the CMIP5 model simulations show more of a difference in the number of malaria cases between the south and the north. Malaria transmission is much higher in the south than in the north. However, the results predicted by the models on the occurrence of malaria by 2100 show differences between the RCP8.5 scenario, considered a high emission scenario, and the RCP4.5 scenario, considered an intermediate mitigation scenario. The CanESM2, CMCC-CM, CMCC-CMS, inmcm4, and IPSL-CM5B models predict decreases with the RCP4.5 scenario. However, ACCESS1-3, CSIRO, NRCM-CM5, GFDL-CM3, GFDL-ESM2G, and GFDL-ESM2M predict increases in malaria under all scenarios (RCP4.5 and RCP8.5). The projected decrease in malaria in the future with these models is much more visible in the RCP8.5 scenario. The results of this study are of paramount importance in the climate-health field. These results will assist in decision-making and will allow for the establishment of preventive surveillance systems for local climate-sensitive diseases, including malaria, in the targeted regions of Senegal.

Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016-2018: a spatial temporal analysis

Background

Temperature, precipitation, relative humidity (RH), and Normalized Different Vegetation Index (NDVI), influence malaria transmission dynamics. However, an understanding of interactions between socioeconomic indicators, environmental factors and malaria incidence can help design interventions to alleviate the high burden of malaria infections on vulnerable populations. Our study thus aimed to investigate the socioeconomic and climatological factors influencing spatial and temporal variability of malaria infections in Mozambique.

Methods

We used monthly malaria cases from 2016 to 2018 at the district level. We developed an hierarchical spatial-temporal model in a Bayesian framework. Monthly malaria cases were assumed to follow a negative binomial distribution. We used integrated nested Laplace approximation (INLA) in R for Bayesian inference and distributed lag nonlinear modeling (DLNM) framework to explore exposure-response relationships between climate variables and risk of malaria infection in Mozambique, while adjusting for socioeconomic factors.

Results

A total of 19,948,295 malaria cases were reported between 2016 and 2018 in Mozambique. Malaria risk increased with higher monthly mean temperatures between 20 and 29°C, at mean temperature of 25°C, the risk of malaria was 3.45 times higher (RR 3.45 [95%CI: 2.37-5.03]). Malaria risk was greatest for NDVI above 0.22. The risk of malaria was 1.34 times higher (1.34 [1.01-1.79]) at monthly RH of 55%. Malaria risk reduced by 26.1%, for total monthly precipitation of 480 mm (0.739 [95%CI: 0.61-0.90]) at lag 2 months, while for lower total monthly precipitation of 10 mm, the risk of malaria was 1.87 times higher (1.87 [1.30-2.69]). After adjusting for climate variables, having lower level of education significantly increased malaria risk (1.034 [1.014-1.054]) and having electricity (0.979 [0.967-0.992]) and sharing toilet facilities (0.957 [0.924-0.991]) significantly reduced malaria risk.

Conclusion

Our current study identified lag patterns and association between climate variables and malaria incidence in Mozambique. Extremes in climate variables were associated with an increased risk of malaria transmission, peaks in transmission were varied. Our findings provide insights for designing early warning, prevention, and control strategies to minimize seasonal malaria surges and associated infections in Mozambique a region where Malaria causes substantial burden from illness and deaths.

Malaria in Senegal: Recent and Future Changes Based on Bias-Corrected CMIP6 Simulations

Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on aspects of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite’s reproductive cycle inside the mosquito. The rainfall intensity and frequency modulate the mosquito population’s development intensity. In this study, the Liverpool Malaria Model (LMM) was used to simulate the spatiotemporal variation of malaria incidence in Senegal. The simulations were based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and the biased-corrected CMIP6 model data, separating historical simulations and future projections for three Shared Socio-economic Pathways scenarios (SSP126, SSP245, and SSP585). Our results highlight a strong increase in temperatures, especially within eastern Senegal under the SSP245 but more notably for the SSP585 scenario. The ability of the LMM model to simulate the seasonality of malaria incidence was assessed for the historical simulations. The model revealed a period of high malaria transmission between September and November with a maximum reached in October, and malaria results for historical and future trends revealed how malaria transmission will change. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and the extreme scenario. This study is important for the planning, prioritization, and implementation of malaria control activities in Senegal.