Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model

Disentangling the seasonality effects of malaria transmission in the Brazilian Amazon basin

The evidence of seasonal patterns in malaria epidemiology in the Brazilian Amazon basin indicates the need for a thorough investigation of seasonality in this last and heterogeneous region. Additionally, since these patterns are linked to climate variables, malaria models should also incorporate them. This study applies wavelet analysis to incidence data from 2003 to 2020 in the Epidemiological Surveillance System for Malaria (SIVEP-Malaria) database. A mathematical model with climate-dependent parametrization is proposed to study counts of malaria cases over time based on notification data, temperature and rainfall. The wavelet analysis reveals marked seasonality in states Amazonas and Amapá throughout the study period, and from 2003 to 2012 in Pará. However, these patterns are not as marked in other states such as Acre and Pará in more recent years. The wavelet coherency analysis indicates a strong association between incidence and temperature, especially for the municipalities of Macapá and Manaus, and a similar association for rainfall. The mathematical model fits well with the observed temporal trends in both municipalities. Studies on climate-dependent mathematical models provide a good assessment of the baseline epidemiology of malaria. Additionally, the understanding of seasonality effects and the application of models have great potential as tools for studying interventions for malaria control.

The Impact of Climatic Factors on Temporal Mosquito Distribution and Population Dynamics in an Area Targeted for Sterile Insect Technique Pilot Trials

It is widely accepted that climate affects the mosquito life history traits; however, its precise role in determining mosquito distribution and population dynamics is not fully understood. This study aimed to investigate the influence of various climatic factors on the temporal distribution of Anopheles arabiensis populations in Mamfene, South Africa between 2014 and 2019. Time series analysis, wavelet analysis, cross-correlation analysis, and regression model combined with the autoregressive integrated moving average (ARIMA) model were utilized to assess the relationship between climatic factors and An. arabiensis population density. In total 3826 adult An. arabiensis collected was used for the analysis. ARIMA (0, 1, 2) (0, 0, 1)12 models closely described the trends observed in An. arabiensis population density and distribution. The wavelet coherence and time-lagged correlation analysis showed positive correlations between An. arabiensis population density and temperature (r = 0.537 ), humidity (r = 0.495) and rainfall (r = 0.298) whilst wind showed negative correlations (r = -0.466). The regression model showed that temperature (p = 0.00119), rainfall (p = 0.0436), and humidity (p = 0.0441) as significant predictors for forecasting An. arabiensis abundance. The extended ARIMA model (AIC = 102.08) was a better fit for predicting An. arabiensis abundance compared to the basic model. Anopheles arabiensis still remains the predominant malaria vector in the study area and climate variables were found to have varying effects on the distribution and abundance of An. arabiensis. This necessitates other complementary vector control strategies such as the Sterile Insect Technique (SIT) which involves releasing sterile males into the environment to reduce mosquito populations. This requires timely mosquito and climate information to precisely target releases and enhance the effectiveness of the program, consequently reducing the malaria risk.

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.

Optimal control and cost-effectiveness analysis of age-structured malaria model with asymptomatic carrier and temperature variability

This paper presents an age-structured mathematical model for malaria transmission dynamics with asymptomatic carrier and temperature variability. The temperature variability function is fitted to the temperature data, and the malaria model is then fitted to the malaria cases and validated to check its suitability. Time-dependent controls were considered, including Long Lasting Insecticide Nets, treatment of symptomatic, screening and treatment of asymptomatic carriers and spray of insecticides. Pontryagin's Maximum Principle is used to derive the necessary conditions for optimal control of the disease. The numerical simulations of the optimal control problem reveal that the strategy involving the combination of all four controls is the most effective in reducing the number of infected individuals. Furthermore, the cost-effectiveness analysis shows that treatment of symptomatic, screening and treatment of asymptomatic carriers and insecticide spraying is the most cost-effective strategy to implement to control malaria transmission when available resources are limited.

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.

Assessment of environmental variability on malaria transmission in a malaria-endemic rural dry zone locality of Sri Lanka: The wavelet approach

Malaria is a global public health concern and its dynamic transmission is still a complex process. Malaria transmission largely depends on various factors, including demography, geography, vector dynamics, parasite reservoir, and climate. The dynamic behaviour of malaria transmission has been explained using various statistical and mathematical methods. Of them, wavelet analysis is a powerful mathematical technique used in analysing rapidly changing time-series to understand disease processes in a more holistic way. The current study is aimed at identifying the pattern of malaria transmission and its variability with environmental factors in Kataragama, a malaria-endemic dry zone locality of Sri Lanka, using a wavelet approach. Monthly environmental data including total rainfall and mean water flow of the “Menik Ganga” river; mean temperature, mean minimum and maximum temperatures and mean relative humidity; and malaria cases in the Kataragama Medical Officer of Health (MOH) area were obtained from the Department of Irrigation, Department of Meteorology and Malaria Research Unit (MRU) of University of Colombo, respectively, for the period 1990 to 2005. Wavelet theory was applied to analyze these monthly time series data. There were two significant periodicities in malaria cases during the period of 1992–1995 and 1999–2000. The cross-wavelet power spectrums revealed an anti-phase correlation of malaria cases with mean temperature, minimum temperature, and water flow of “Menik Ganga” river during the period 1991–1995, while the in-phase correlation with rainfall is noticeable only during 1991–1992. Relative humidity was similarly associated with malaria cases between 1991–1992. It appears that environmental variables have contributed to a higher incidence of malaria cases in Kataragama in different time periods between 1990 and 2005.

Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa

This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman's correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer-December, January, and February (DJF)-can be explained by spring-September, October, and November (SON)-rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.

Exploring the Influence of Daily Climate Variables on Malaria Transmission and Abundance of Anopheles arabiensis over Nkomazi Local Municipality, Mpumalanga Province, South Africa

The recent resurgence of malaria incidence across epidemic regions in South Africa has been linked to climatic and environmental factors. An in-depth investigation of the impact of climate variability and mosquito abundance on malaria parasite incidence may therefore offer useful insight towards the control of this life-threatening disease. In this study, we investigate the influence of climatic factors on malaria transmission over Nkomazi Municipality. The variability and interconnectedness between the variables were analyzed using wavelet coherence analysis. Time-series analyses revealed that malaria cases significantly declined after the outbreak in early 2000, but with a slight increase from 2015. Furthermore, the wavelet coherence and time-lagged correlation analyses identified rainfall and abundance of Anopheles arabiensis as the major variables responsible for malaria transmission over the study region. The analysis further highlights a high malaria intensity with the variables from 1998-2002, 2004-2006, and 2010-2013 and a noticeable periodicity value of 256-512 days. Also, malaria transmission shows a time lag between one month and three months with respect to mosquito abundance and the different climatic variables. The findings from this study offer a better understanding of the importance of climatic factors on the transmission of malaria. The study further highlights the significant roles of An. arabiensis on malaria occurrence over Nkomazi. Implementing the mosquito model to predict mosquito abundance could provide more insight into malaria elimination or control in Africa.