Sensitivity of vegetation to climate variability and its implications for malaria risk in Baringo, Kenya

Combining Morphological and Molecular Tools Can Enhance Tick Species Identification for Improved Tick-Borne Disease Surveillance Among Pastoral Communities in Kenya

Background: Ticks are ecto-parasites of domestic animals, rodents, and wildlife living for periods at a time on one or more vertebrate hosts. They are important vectors of viral, bacterial, or parasitic diseases in livestock and humans. Crimean-Congo haemorrhagic fever virus and the spotted fever rickettsiae are some of the tick-borne diseases of public health importance reported in Kenya. Their distribution and public health risks among communities, especially pastoralists, remain poorly characterized due to limited surveillance, affected partly by inadequate capacity for tick identification arising from a limited number of skilled taxonomists. Materials and Methods: The aim of this survey was to identify tick species currently circulating in different livestock hosts in northern Kenya. Ticks were sampled from cattle, sheep, goats, and camels in Turkana, Isiolo, Baringo, and West Pokot counties, and differential identification was carried out using morphological identification keys followed by molecular characterization based on the cytochrome c oxidase I gene (cox1). Haplotypes were determined using the DnaSP v6 software and phylogenetic relationships inferred using the maximum likelihood algorithm. Results: A total of 12,206 ticks were collected, from Turkana (45.4%), Isiolo (23.1%), Baringo (22.7%), and West Pokot (8.8%) counties in Kenya. Ten species were confirmed by molecular analysis; H. rufipes, H. impeltatum, H. dromedarii, R. pravus, R. camicasi, R. pulchellus, R. evertsi evertsi, A. variegatum, A. gemma, and A. lepidum. There was no disparity in the morphological and molecular identification of Amblyomma species. However, molecular analysis provided insight into the complexity of morphological identification especially among Hyalomma and Rhipicephalus species. High haplotype diversities (0.857-1.000) and low nucleotide diversities (0.00719-0.06319) were observed in all the tick samples tested. Conclusion: The findings highlight the diversity of tick species in dry pastoral ecologies in Kenya and the importance of confirming morphological identification by molecular analysis thus contributing to accurate mapping of tick-borne disease distribution and risk.

Exploring malaria prediction models in Togo: a time series forecasting by health district and target group

OBJECTIVES

Integrating malaria prediction models into malaria control strategies can help to anticipate the response to seasonal epidemics. This study aimed to explore the possibility of using routine malaria data and satellite-derived climate data to forecast malaria cases in Togo.

METHODS

Generalised additive (mixed) models were developed to forecast the monthly number of malaria cases in 40 health districts and three target groups. Routinely collected malaria data from 2013 to 2016 and meteorological and vegetation data with a time lag of 1 or 2 months were used for model training, while the year 2017 was used for model testing. Two methods for selecting lagged meteorological and environmental variables were compared: a first method based on statistical approach ('SA') and a second method based on biological reasoning ('BR'). Both methods were applied to obtain a model per target group and health district and a mixed model per target group and health region with the health district as a random effect. The predictive skills of the four models were compared for each health district and target group.

RESULTS

The most selected predictors in the models per district for the 'SA' method were the normalised difference vegetation index, minimum temperature and mean temperature. The 'SA' method provided the most accurate models for the training period, except for some health districts in children ≥5 years old and adults and in pregnant women. The most accurate models for the testing period varied by health district and target group, provided either by the 'SA' method or the 'BR' method. Despite the development of models with four different approaches, the number of malaria cases was inaccurately forecasted.

CONCLUSIONS

These models cannot be used as such in malaria control activities in Togo. The use of finer spatial and temporal scales and non-environmental data could improve malaria prediction.

Long-lasting household damage from Cyclone Idai increases malaria risk in rural western Mozambique

Cyclone Idai in 2019 was one of the worst tropical cyclones recorded in the Southern Hemisphere. The storm caused catastrophic damage and led to a humanitarian crisis in Mozambique. The affected population suffered a cholera epidemic on top of housing and infrastructure damage and loss of life. The housing and infrastructure damage sustained during Cyclone Idai still has not been addressed in all affected communities. This is of grave concern because storm damage results in poor housing conditions which are known to increase the risk of malaria. Mozambique has the 4th highest malaria prevalence in sub-Saharan Africa and is struggling to control malaria in most of the country. We conducted a community-based cross-sectional survey in Sussundenga Village, Manica Province, Mozambique in December 2019-February 2020. We found that most participants (64%) lived in households that sustained damage during Cyclone Idai. The overall malaria prevalence was 31% measured by rapid diagnostic test (RDT). When controlling for confounding variables, the odds of malaria infection was nearly threefold higher in participants who lived in households damaged by Cyclone Idai nearly a year after the storm. This highlights the need for long-term disaster response to improve the efficiency and success of malaria control efforts.

Global malaria infection risk from climate change

As a long-standing public health issue, malaria still severely affects many parts of the world, especially Africa. With greenhouse gas emissions, temperatures continue to rise. Based on diverse shared socioeconomic pathways (SSPs), future temperatures can be estimated. However, the impacts of climate change on malaria infection rates in all epidemic regions are unknown. Here, we estimate the differences in global malaria infection rates predicted under different SSPs during several periods as well as malaria infection case changes (MICCs) resulting from those differences. Our results indicate that the global MICCs resulting from the conversion from SSP1-2.6 to SSP2-4.5, to SSP3-7.0, and to SSP5-8.5 are 6.506 (with a 95% uncertainty interval [UI] of 6.150-6.861) million, 3.655 (3.416-3.894) million, and 2.823 (2.635-3.012) million, respectively, from 2021 to 2040; these values represent increases of 2.699%, 1.517%, and 1.171%, respectively, compared to the 241 million infection cases reported in 2020. Temperatures increases will adversely affect malaria the most in Africa during the 2021-2040 period. From 2081 to 2100, the MICCs obtained for the three scenario shifts listed above are -79.109 (-83.626 to -74.591) million, -238.337 (-251.920 to -0.141) million, and -162.692 (-174.628 to -150.757) million, corresponding to increases of -32.825%, -98.895%, and -67.507%, respectively. Climate change will increase the danger and risks associated with malaria in the most vulnerable regions in the near term, thus aggravating the difficulty of eliminating malaria. Reducing GHG emissions is a potential pathway to protecting people from malaria.

Associations between environmental covariates and temporal changes in malaria incidence in high transmission settings of Uganda: a distributed lag nonlinear analysis

BACKGROUND

Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda.

METHODS

This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs' catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence.

RESULTS

Overall, the median (range) monthly temperature was 30 °C (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag-month 4.

CONCLUSIONS

In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

Vector role and human biting activity of Anophelinae mosquitoes in different landscapes in the Brazilian Amazon

BACKGROUND

Environmental disturbance, deforestation and socioeconomic factors all affect malaria incidence in tropical and subtropical endemic areas. Deforestation is the major driver of habitat loss and fragmentation, which frequently leads to shifts in the composition, abundance and spatial distribution of vector species. The goals of the present study were to: (i) identify anophelines found naturally infected with Plasmodium; (ii) measure the effects of landscape on the number of Nyssorhynchus darlingi, presence of Plasmodium-infected Anophelinae, human biting rate (HBR) and malaria cases; and (iii) determine the frequency and peak biting time of Plasmodium-infected mosquitoes and Ny. darlingi.

METHODS

Anopheline mosquitoes were collected in peridomestic and forest edge habitats in seven municipalities in four Amazon Brazilian states. Females were identified to species and tested for Plasmodium by real-time PCR. Negative binomial regression was used to measure any association between deforestation and number of Ny. darlingi, number of Plasmodium-infected Anophelinae, HBR and malaria. Peak biting time of Ny. darlingi and Plasmodium-infected Anophelinae were determined in the 12-h collections. Binomial logistic regression measured the association between presence of Plasmodium-infected Anophelinae and landscape metrics and malaria cases.

RESULTS

Ninety-one females of Ny. darlingi, Ny. rangeli, Ny. benarrochi B and Ny. konderi B were found to be infected with Plasmodium. Analysis showed that the number of malaria cases and the number of Plasmodium-infected Anophelinae were more prevalent in sites with higher edge density and intermediate forest cover (30-70%). The distance of the drainage network to a dwelling was inversely correlated to malaria risk. The peak biting time of Plasmodium-infected Anophelinae was 00:00-03:00 h. The presence of Plasmodium-infected mosquitoes was higher in landscapes with > 13 malaria cases.

CONCLUSIONS

Nyssorhynchus darlingi, Ny. rangeli, Ny. benarrochi B and Ny. konderi B can be involved in malaria transmission in rural settlements. The highest fraction of Plasmodium-infected Anophelinae was caught from midnight to 03:00 h. In some Amazonian localities, the highest exposure to infectious bites occurs when residents are sleeping, but transmission can occur throughout the night. Forest fragmentation favors increases in both malaria and the occurrence of Plasmodium-infected mosquitoes in peridomestic habitat. The use of insecticide-impregnated mosquito nets can decrease human exposure to infectious Anophelinae and malaria transmission.

Significant factors associated with malaria spread in Thailand: a cross-sectional study

Purpose

This paper aims to uncover new factors that influence the spread of malaria.

Design/methodology/approach

The historical data related to malaria were collected from government agencies. Later, the data were cleaned and standardized before passing through the analysis process. To obtain the simplicity of these numerous factors, the first procedure involved in executing the factor analysis where factors' groups related to malaria distribution were determined. Therefore, machine learning was deployed, and the confusion matrices are computed. The results from machine learning techniques were further analyzed with logistic regression to study the relationship of variables affecting malaria distribution.

Findings

This research can detect 28 new noteworthy factors. With all the defined factors, the logistics model tree was constructed. The precision and recall of this tree are 78% and 82.1%, respectively. However, when considering the significance of all 28 factors under the logistic regression technique using forward stepwise, the indispensable factors have been found as the number of houses without electricity (houses), number of irrigation canals (canals), number of shallow wells (places) and number of migrated persons (persons). However, all 28 factors must be included to obtain high accuracy in the logistics model tree.

Originality/value

This paper may lead to highly-efficient government development plans, including proper financial management for malaria control sections. Consequently, the spread of malaria can be reduced naturally.

Multi-Indicator and Multistep Assessment of Malaria Transmission Risks in Western Kenya

Malaria risk factor assessment is a critical step in determining cost-effective intervention strategies and operational plans in a regional setting. We develop a multi-indicator multistep approach to model the malaria risks at the population level in western Kenya. We used a combination of cross-sectional seasonal malaria infection prevalence, vector density, and cohort surveillance of malaria incidence at the village level to classify villages into malaria risk groups through unsupervised classification. Generalized boosted multinomial logistics regression analysis was performed to determine village-level risk factors using environmental, biological, socioeconomic, and climatic features. Thirty-six villages in western Kenya were first classified into two to five operational groups based on different combinations of malaria risk indicators. Risk assessment indicated that altitude accounted for 45-65% of all importance value relative to all other factors; all other variable importance values were < 6% in all models. After adjusting by altitude, villages were classified into three groups within distinct geographic areas regardless of the combination of risk indicators. Risk analysis based on altitude-adjusted classification indicated that factors related to larval habitat abundance accounted for 63% of all importance value, followed by geographic features related to the ponding effect (17%), vegetation cover or greenness (15%), and the number of bed nets combined with February temperature (5%). These results suggest that altitude is the intrinsic factor in determining malaria transmission risk in western Kenya. Malaria vector larval habitat management, such as habitat reduction and larviciding, may be an important supplement to the current first-line vector control tools in the study area.

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.

Exploring the Relevance of Green Space and Epidemic Diseases Based on Panel Data in China from 2007 to 2016

Urban green space has been proven effective in improving public health in the contemporary background of planetary urbanization. There is a growing body of literature investigating the relationship between non-communicable diseases (NCDs) and green space, whereas seldom has the correlation been explored between green space and epidemics, such as dysentery, tuberculosis, and malaria, which still threaten the worldwide situation of public health. Meanwhile, most studies explored healthy issues with the general green space, public green space, and green space coverage, respectively, among which the different relevance has been rarely explored. This study aimed to examine and compare the relevance between these three kinds of green space and incidences of the three types of epidemic diseases based on the Panel Data Model (PDM) with the time series data of 31 Chinese provinces from 2007 to 2016. The results indicated that there exists different, or even opposite, relevance between various kinds of green space and epidemic diseases, which might be associated with the process of urban sprawl in rapid urbanization in China. This paper provides a reference for re-thinking the indices of green space in building healthier and greener cities.