Stochastic lattice-based modelling of malaria dynamics

Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission Using a Hydrology-Based Model

A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub-Saharan Africa. Irrigation has been associated with increased malaria risk, but risk prediction remains difficult due to the heterogeneity of irrigation and the environment. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats. By coupling a hydrologic model with an agent-based malaria model for a sugarcane plantation site in Arjo, Ethiopia, we demonstrated how incorporating hydrologic processes to estimate larval habitats can affect malaria transmission. Using the coupled model, we then examined the impact of an existing irrigation scheme on malaria transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitat area by around two-fold and resulted in reduction in malaria transmission by 60%. In addition, irrigation increased all habitat types in the dry season by up to 7.4 times. It converted temporary and semi-permanent habitats to permanent habitats during the rainy season, which grew by about 24%. Consequently, malaria transmission was sustained all-year round and intensified during the main transmission season, with the peak shifted forward by around 1 month. Lastly, we evaluated the spatiotemporal distribution of adult vectors under the effect of irrigation by resolving habitat heterogeneity. These findings could help larval source management by identifying transmission hotspots and prioritizing resources for malaria elimination planning.

The current landscape of software tools for the climate-sensitive infectious disease modelling community

Climate-sensitive infectious disease modelling is crucial for public health planning and is underpinned by a complex network of software tools. We identified only 37 tools that incorporated both climate inputs and epidemiological information to produce an output of disease risk in one package, were transparently described and validated, were named (for future searching and versioning), and were accessible (ie, the code was published during the past 10 years or was available on a repository, web platform, or other user interface). We noted disproportionate representation of developers based at North American and European institutions. Most tools (n=30 [81%]) focused on vector-borne diseases, and more than half (n=16 [53%]) of these tools focused on malaria. Few tools (n=4 [11%]) focused on food-borne, respiratory, or water-borne diseases. The under-representation of tools for estimating outbreaks of directly transmitted diseases represents a major knowledge gap. Just over half (n=20 [54%]) of the tools assessed were described as operationalised, with many freely available online.

An Overview of Malaria Transmission Mechanisms, Control, and Modeling

In sub-Saharan Africa, malaria is a leading cause of mortality and morbidity. As a result of the interplay between many factors, the control of this disease can be challenging. However, few studies have demonstrated malaria's complexity, control, and modeling although this perspective could lead to effective policy recommendations. This paper aims to be a didactic material providing the reader with an overview of malaria. More importantly, using a system approach lens, we intend to highlight the debated topics and the multifaceted thematic aspects of malaria transmission mechanisms, while showing the control approaches used as well as the model supporting the dynamics of malaria. As there is a large amount of information on each subject, we have attempted to provide a basic understanding of malaria that needs to be further developed. Nevertheless, this study illustrates the importance of using a multidisciplinary approach to designing next-generation malaria control policies.

Using models to provide rapid programme support for California's efforts to suppress Huanglongbing disease of citrus

We describe a series of operational questions posed during the state-wide response in California to the arrival of the invasive citrus disease Huanglongbing. The response is coordinated by an elected committee from the citrus industry and operates in collaboration with the California Department of Food and Agriculture, which gives it regulatory authority to enforce the removal of infected trees. The paper reviews how surveillance for disease and resource allocation between detection and delimitation have been addressed, based on epidemiological principles. In addition, we describe how epidemiological analyses have been used to support rule-making to enact costly but beneficial regulations and we highlight two recurring themes in the programme support work: (i) data are often insufficient for quantitative analyses of questions and (ii) modellers and decision-makers alike may be forced to accept the need to make decisions on the basis of simple or incomplete analyses that are subject to considerable uncertainty. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

The stationary distribution and stochastic persistence for a class of disease models: Case study of malaria

This paper presents a nonlinear family of stochastic SEIRS models for diseases such as malaria in a highly random environment with noises from the disease transmission and natural death rates, and also from the random delays of the incubation and immunity periods. Improved analytical methods and local martingale characterizations are applied to find conditions for the disease to persist near an endemic steady state, and also for the disease to remain permanently in the system over time. Moreover, the ergodic stationary distribution for the stochastic process describing the disease dynamics is defined, and the statistical characteristics of the distribution are given numerically. The results of this study show that the disease will persist and become permanent in the system, regardless of (1) whether the noises are from the disease transmission rate and/or from the natural death rates or (2) whether the delays in the system are constant or random for individuals in the system. Furthermore, it is shown that “weak” noise is associated with the existence of an endemic stationary distribution for the disease, while “strong” noise is associated with extinction of the population over time. Numerical simulation examples for Plasmodium vivax malaria are given.

Assessing the role of human mobility on malaria transmission

South Sudan accounts for a large proportion of all annual malaria cases in Africa. In recent years, the country has witnessed an unprecedented number of people on the move, refugees, internally displaced people, people who have returned to their counties or areas of origin, stateless people and other populations of concern, posing challenges to malaria control. Thus, one can claim that human mobility is one of the contributing factors to the resurgence of malaria. The aim of this paper is to assess the impact of human mobility on the burden of malaria disease in South Sudan. For this, we formulate an SIR-type model that describes the transmission dynamics of malaria disease between multiple patches. The proposed model is a system of stochastic differential equations consisting of ordinary differential equations perturbed by a stochastic Wiener process. For the deterministic part of the model, we calculate the basic reproduction number. Concerning the whole stochastic model, we use the maximum likelihood approach to fit the model to weekly malaria data of 2011 from Central Equatoria State, Western Bahr El Ghazal State and Warrap State. Using the parameters estimated on the fitted model, we simulate the future observation of the disease pattern. The disease was found to persist in the low transmission patches when there is human inflow in these patches and although the intervention coverage reaches 75%.

Predicting the direct and indirect impacts of climate change on malaria in coastal Kenya

Background

The transmission of malaria is highly variable and depends on a range of climatic and anthropogenic factors. This study investigates the combined, i.e. direct and indirect, impacts of climate change on the dynamics of malaria through modifications in: (i) the sporogonic cycle of Plasmodium induced by air temperature increase, and (ii) the life cycle of Anopheles vector triggered by changes in natural breeding habitat arising from the altered moisture dynamics resulting from acclimation responses of vegetation under climate change. The study is performed for a rural region in Kilifi county, Kenya.

Methods and findings

We use a stochastic lattice-based malaria (SLIM) model to make predictions of changes in Anopheles vector abundance, the life cycle of Plasmodium parasites, and thus malaria transmission under projected climate change in the study region. SLIM incorporates a nonlinear temperature-dependence of malaria parasite development to estimate the extrinsic incubation period of Plasmodium. It is also linked with a spatially distributed eco-hydrologic modeling framework to capture the impacts of climate change on soil moisture dynamics, which served as a key determinant for the formation and persistence of mosquito larval habitats on the land surface. Malaria incidence data collected from 2008 to 2013 is used for SLIM model validation. Projections of climate change and human population for the region are used to run the models for prediction scenarios.

Under elevated atmospheric CO₂ concentration ([CO₂]) only, modeled results reveal wetter soil moisture in the root zone due to the suppression of transpiration from vegetation acclimation, which increases the abundance of Anopheles vectors and the risk of malaria. When air temperature increases are also considered along with elevated [CO₂], the life cycle of Anopheles vector and the extrinsic incubation period of Plasmodium parasites are shortened nonlinearly. However, the reduction of soil moisture resulting from higher evapotranspiration due to air temperature increase also reduces the larval habitats of the vector. Our findings show the complicated role of vegetation acclimation under elevated [CO₂] on malaria dynamics and indicate an indirect but ignored impact of air temperature increase on malaria transmission through reduction in larval habitats and vector density.

Conclusions

Vegetation acclimation triggered by elevated [CO₂] under climate change increases the risk of malaria. In addition, air temperature increase under climate change has opposing effects on mosquito larval habitats and the life cycles of both Anopheles vectors and Plasmodium parasites. The indirect impacts of temperature change on soil moisture dynamics are significant and should be weighed together with the direct effects of temperature change on the life cycles of mosquitoes and parasites for future malaria prediction and control.