The neglected role of relative humidity in the interannual variability of urban malaria in Indian cities

HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003-2020

Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R2 values for all six moisture indices exceeding 0.96 and root mean square error and mean absolute error values within a reasonable range. The dataset exhibits high consistency with in situ observations over various spatial and temporal regimes, demonstrating broad applicability and strong reliability.

Effects of climate change and human activities on vector-borne diseases

Vector-borne diseases are transmitted by haematophagous arthropods (for example, mosquitoes, ticks and sandflies) to humans and wild and domestic animals, with the largest burden on global public health disproportionately affecting people in tropical and subtropical areas. Because vectors are ectothermic, climate and weather alterations (for example, temperature, rainfall and humidity) can affect their reproduction, survival, geographic distribution and, consequently, ability to transmit pathogens. However, the effects of climate change on vector-borne diseases can be multifaceted and complex, sometimes with ambiguous consequences. In this Review, we discuss the potential effects of climate change, weather and other anthropogenic factors, including land use, human mobility and behaviour, as possible contributors to the redistribution of vectors and spread of vector-borne diseases worldwide.

Identification of breeding habitats and kdr mutations in Anopheles spp. in South Korea

BACKGROUND

Malaria is still endemic in South Korea. However, limited information is available on the current Anopheles breeding sites and the occurrence of insecticide resistance-associated genetic mutations and their distribution needed to control the malaria vector efficiently.

METHODS

This study explored breeding sites of Anopheline adults in Gimpo-si, near the demilitarized zone (DMZ) in Gyeonggi-do province, South Korea, from 2022 to 2023. Genetic diversity was investigated based on the internal transcribed spacer (ITS2), cytochrome c oxidase subunit I (COI), and knockdown resistance (kdr) genes of Anopheles mosquitoes. A natural environment associated with the seasonal abundance of Anopheles larvae was characterized.

RESULTS

Two breeding sites of Anopheles larvae and adults were found at a stream margin or shallow freshwater near the forest in Wolgot-myeon in Gimpo-si without cattle shed within 1 km and in Naega-myeon in Ganghwa-gun with cow shed within 100 m in 2022 and 2023, respectively. Both sites were located between the newly cultivated lands and the forest. Besides, both breeding sites were in the valley at a slight elevation of 60-70 m from ground lands and maintained the shadow all day. Overall, the Wolgot-myeon breeding site showed various Anopheles spp. larvae, including Anopheles sinensis. Naega-myeon, an additional breeding site found in 2023, had Anopheles sineroides larvae, and approximately 59.7% (89/149) of An. sinensis adults inhabited within a 100-m distance. The total collection, including larvae and adults, revealed that An. sinensis, Anopheles pullus, Anopheles kleini, An. sineroides, Anopheles belenrae, and Anopheles lindesayi accounted for 44.2% (118/267), 0.7% (2/267), 0.7% (2/267), 22.1% (59/267), 1.9% (5/267), and 30.3% (81/267), respectively. Furthermore, various kdr mutant genotypes (F/F, C/C, L/F, L/C and F/C) in An. sinensis, and the first kdr allele mutant (L/F1014) in An. belenrae were identified in South Korea.

CONCLUSIONS

Two breeding sites of Anopheles larvae were studied in Wolgot-myeon and Naega-myeon. Various Anopheles spp. larvae were detected in both habitats, but overall, An. sinensis was the most prevalent adults in both study sites. The occurrence of kdr allele mutant of An. belenrae in South Korea was reported. Rigorous larvae monitoring of Anopheles spp., continuously updating information on Anopheles breeding sites, and understanding the environmental conditions of Anopheles habitats are required to develop an effective malaria control programme in South Korea.

Quantifying climatic and socioeconomic drivers of urban malaria in Surat, India: a statistical spatiotemporal modelling study

BACKGROUND

Cities are becoming increasingly important habitats for mosquito vectors of disease. The pronounced heterogeneity of urban landscapes challenges our understanding of the effects of climate and socioeconomic factors on mosquito-borne disease dynamics at different spatiotemporal scales. Here, we quantify the impact of climatic and socioeconomic factors on urban malaria risk, using an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, northwest India.

METHODS

We analysed 10 years of monthly P falciparum cases resolved at three nested spatial resolutions (seven zones, 32 units, and 478 worker units) with a Bayesian hierarchical mixed model that incorporates the effects of population density, poverty, relative humidity, and temperature, in addition to random effects (structured and unstructured). To reduce dimensionality and avoid correlation of covariates, socioeconomic variables from survey data were summarised into main axes of variation using principal component analysis. With model selection, we identified the main drivers of spatiotemporal variation in malaria incidence rates at each of the three spatial resolutions. We also compared observations to model-fitted cases by quantifying the percentage of predictions within five discrete levels of malaria risk.

FINDINGS

The spatial variation of urban malaria cases was stationary over time, whereby locations with high and low yearly cases remained largely consistent across years. Local socioeconomic variation could be summarised with three principal components accounting for approximately 80% of the variance. The model that incorporated local temperature and relative humidity together with two of these principal components, largely representing population density and poverty, best explained monthly malaria patterns in models formulated at the three different spatial scales. As model resolution increased, the effect size of humidity decreased, whereas those of temperature and the principal component associated with population density increased. Model predictions accurately captured aggregated total monthly cases for the city; in space-time, they more closely matched observations at the intermediate scale, with around 57% of units estimated to fall in the observed category on average across years. The mean absolute error was lower at the intermediate level, showing that this is the best aggregation level to predict the space-time dynamics of malaria incidence rates across the city with the selected model.

INTERPRETATION

This statistical modelling framework provides a basis for development of a climate-driven early warning system for urban malaria for the units of Surat, including spatially explicit prediction of malaria risk several weeks to months in advance. Results indicate environmental and socioeconomic covariates for which further measurement at high resolution should lead to model improvement. Advanced warning combined with local surveillance and knowledge of disease hotspots within the city could inform targeted intervention as part of urban malaria elimination efforts.

FUNDING

US National Institutes of Health.

Positional Isomers of Covalent Organic Frameworks for Indoor Humidity Regulation

Humidity is one of the most important indicators affecting human health. Here, a pair of covalent organic frameworks (COFs) of positional isomers (p-COF and o-COF) for indoor humidity regulation is reported. Although p-COF and o-COF have the same sql topology and pore size, they exhibit different water adsorption behaviors due to the subtle differences in water adsorption sites. Particularly, o-COF exhibits a steep adsorption isotherm in the range of 45-65% RH with a hysteresis loop, which is perfectly suitable for indoor humidity regulation. In the laboratory experiment, when the humidity of the external environment is 20-75% RH, o-COF can control the humidity of the room in the range of 45-60% RH. o-COF has shown great potential as a dual humidification/dehumidification adsorbent for indoor humidity regulation.

Inference of transmission dynamics and retrospective forecast of invasive meningococcal disease

The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

Geo-environmental factors and the effectiveness of mulberry leaf extract in managing malaria

Malaria prevalence has become medically important and a socioeconomic impediment for the endemic regions, including Purulia, West Bengal. Geo-environmental variables, humidity, altitude, and land use patterns are responsible for malaria. For surveillance of the endemic nature of Purulia's blocks, statistical and spatiotemporal factors analysis have been done here. Also, a novel approach for the Pf malaria treatment using methanolic leaf extract of Morus alba S1 has significantly reduced the parasite load. The EC50 value (1.852) of the methanolic extract of M. alba S1 with P. falciparum 3D7 strain is close to the EC50 value (0.998) of the standard drug chloroquine with the same chloroquine-sensitive strain. Further studies with an in-silico model have shown successful interaction between DHFR and the phytochemicals. Both 1-octadecyne and oxirane interacted favourably, which was depicted through GC-MS analysis. The predicted binary logistic regression model will help the policy makers for epidemiological surveillance in malaria-prone areas worldwide when substantial climate variables create a circumstance favourable for malaria. From the in vitro and in silico studies, it can be concluded that the methanolic extract of M. alba S1 leaves were proven to have promising antiplasmodial activity. Thus, there is a scope for policy-driven approach for discovering and developing these lead compounds and undermining the rising resistance to the frontline anti-malarial drugs in the world.

Humidity - The overlooked variable in the thermal biology of mosquito-borne disease

Vector-borne diseases cause significant financial and human loss, with billions of dollars spent on control. Arthropod vectors experience a complex suite of environmental factors that affect fitness, population growth and species interactions across multiple spatial and temporal scales. Temperature and water availability are two of the most important abiotic variables influencing their distributions and abundances. While extensive research on temperature exists, the influence of humidity on vector and pathogen parameters affecting disease dynamics are less understood. Humidity is often underemphasized, and when considered, is often treated as independent of temperature even though desiccation likely contributes to declines in trait performance at warmer temperatures. This Perspectives explores how humidity shapes the thermal performance of mosquito-borne pathogen transmission. We summarize what is known about its effects and propose a conceptual model for how temperature and humidity interact to shape the range of temperatures across which mosquitoes persist and achieve high transmission potential. We discuss how failing to account for these interactions hinders efforts to forecast transmission dynamics and respond to epidemics of mosquito-borne infections. We outline future research areas that will ground the effects of humidity on the thermal biology of pathogen transmission in a theoretical and empirical framework to improve spatial and temporal prediction of vector-borne pathogen transmission.

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.

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

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

Simulation of the potential impact of climate change on malaria incidence using artificial neural networks (ANNs)

Climate change can increase the spread of infectious diseases and public health concerns. Malaria is one of the endemic infectious diseases of Iran, whose transmission is strongly influenced by climatic conditions. The effect of climate change on malaria in the southeastern Iran from 2021 to 2050 was simulated by using artificial neural networks (ANNs). Gamma test (GT) and general circulation models (GCMs) were used to determine the best delay time and to generate the future climate model under two distinct scenarios (RCP2.6 and RCP8.5). To simulate the various impacts of climate change on malaria infection, ANNs were applied using daily collected data for 12 years (from 2003 to 2014). The future climate of the study area will be hotter by 2050. The simulation of malaria cases elucidated that there is an intense increasing trend in malaria cases under the RCP8.5 scenario until 2050, with the highest number of infections occurring in the warmer months. Rainfall and maximum temperature were identified as the most influential input variables. Optimum temperatures and increased rainfall provide a suitable environment for the transmission of parasites and cause an intense increase in the number of infection cases with a delay of approximately 90 days. ANNs were introduced as a practical tool for simulating the impact of climate change on the prevalence, geographic distribution, and biological activity of malaria and for estimating the future trend of the disease in order to adopt protective measures in endemic areas.

The synergistic effect of climatic factors on malaria transmission: a predictive approach for northeastern states of India

The northeast region of India is highlighted as the most vulnerable region for malaria. This study attempts to explore the epidemiological profile and quantify the climate-induced influence on malaria cases in the context of tropical states, taking Meghalaya and Tripura as study areas. Monthly malaria cases and meteorological data from 2011 to 2018 and 2013 to 2019 were collected from the states of Meghalaya and Tripura, respectively. The nonlinear associations between individual and synergistic effect of meteorological factors and malaria cases were assessed, and climate-based malaria prediction models were developed using the generalized additive model (GAM) with Gaussian distribution. During the study period, a total of 216,943 and 125,926 cases were recorded in Meghalaya and Tripura, respectively, and majority of the cases occurred due to the infection of Plasmodium falciparum in both the states. The temperature and relative humidity in Meghalaya and temperature, rainfall, relative humidity, and soil moisture in Tripura showed a significant nonlinear effect on malaria; moreover, the synergistic effects of temperature and relative humidity (SI=2.37, RERI=0.58, AP=0.29) and temperature and rainfall (SI=6.09, RERI=2.25, AP=0.61) were found to be the key determinants of malaria transmission in Meghalaya and Tripura, respectively. The developed climate-based malaria prediction models are able to predict the malaria cases accurately in both Meghalaya (RMSE: 0.0889; R²: 0.944) and Tripura (RMSE: 0.0451; R²: 0.884). The study found that not only the individual climatic factors can significantly increase the risk of malaria transmission but also the synergistic effects of climatic factors can drive the malaria transmission multifold. This reminds the policymakers to pay attention to the control of malaria in situations with high temperature and relative humidity and high temperature and rainfall in Meghalaya and Tripura, respectively.

Changes in the associations between malaria incidence and climatic factors across malaria endemic countries in Africa and Asia-Pacific region

Empirical evidence supporting the associations between malaria incidence and climatic factors has remained controversial, buffering the progress in the Global Malaria Program that aims to eliminate 90% of the world malaria burden by 2030. This study has aimed to evaluate the nature and extent at which these relations are maintained across all the malaria endemic countries of Africa and Asia-Pacific region. We have utilized the last two decades of malaria incidence, annual minimum temperature, and annual precipitation time series data (2000-2020) for the two most malaria-impacted regions. These data were fitted in the generalized linear model and the mixed effects model. The results showed that there exists a large heterogeneity in malaria incidence across the countries, and between the regions. Last two decadal tendencies showed significant reductions in the disease burden in almost all the countries in the Asia Pacific, with several exceptions or relatively slowed reductions in the Africa. We found significant changes in the positive linear associations between malaria incidence, annual minimum temperature, and annual precipitation across African countries. Many Asia-Pacific countries namely Bangladesh, Bhutan, Indonesia, South Korea, Nepal, Thailand, Vietnam showed negative effects in the associations with the annual precipitation. This study indicates that increasing temperature within the range of 12-30 °C can generate positive effects on malaria incidence, but the nature and extent of precipitation effects vary across countries and at a large regional scale.

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.