Out of the net: An agent-based model to study human movements influence on local-scale malaria transmission

Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model

BACKGROUND

More details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multiagent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in 2 townships in Myanmar.

METHODS

An agent-based model (ABM) was built to simulate daily travel to and from work based on responses to a travel survey. Key elements for the ABM were land cover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey were used to validate the simulation.

RESULTS

Mobility characteristics for different occupation groups showed that while certain patterns were shared among some groups, there were also patterns that were unique to an occupation group. Forest workers were estimated to be the most mobile occupation group, and also had the highest potential malaria exposure associated with their daily travel in Ann Township. In Singu Township, forest workers were not the most mobile group; however, they were estimated to visit regions that had higher prevalence of malaria infection over other occupation groups.

CONCLUSIONS

Using an ABM to simulate daily travel generated mobility patterns for different occupation groups. These spatial patterns varied by occupation. Our simulation identified occupations at a higher risk of being exposed to malaria and where these exposures were more likely to occur.

A vector-agent approach to (spatiotemporal) movement modelling and reasoning

Modelling a complex system of autonomous individuals moving through space and time essentially entails understanding the (heterogeneous) spatiotemporal context, interactions with other individuals, their internal states and making any underlying causal interrelationships explicit, a task for which agents (including vector-agents) are specifically well-suited. Building on a conceptual model of agent space-time and reasoning behaviour, a design guideline for an implemented vector-agent model is presented. The movement of football players was chosen as it is appropriately constrained in space, time and individual actions. Sensitivity-variability analysis was applied to measure the performance of different configurations of system components on the emergent movement patterns. The model output varied more when the condition of the contextual actors (players' role-areas) was manipulated. The current study shows how agent-based modelling can contribute to our understanding of movement and how causally relevant evidence can be produced, illustrated through a spatiotemporally constrained football case-study.

Advancing climate change health adaptation through implementation science

To date, there are few examples of implementation science studies that help guide climate-related health adaptation. Implementation science is the study of methods to promote the adoption and integration of evidence-based tools, interventions, and policies into practice to improve population health. These studies can provide the needed empirical evidence to prioritise and inform implementation of health adaptation efforts. This Personal View discusses five case studies that deployed disease early warning systems around the world. These cases studies illustrate challenges to deploying early warning systems and guide recommendations for implementation science approaches to enhance future research. We propose theory-informed approaches to understand multilevel barriers, design strategies to overcome those barriers, and analyse the ability of those strategies to advance the uptake and scale-up of climate-related health interventions. These findings build upon previous theoretical work by grounding implementation science recommendations and guidance in the context of real-world practice, as detailed in the case studies.

Statistical Implementations of Agent-Based Demographic Models

A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing due to imperfect detection of individuals, the associated measurement error can be accommodated under certain study designs (e.g. those that involve multiple surveys or replication). However, the interaction of the measurement error and the underlying dynamic process can complicate the implementation of statistical agent-based models (ABMs) for population demography. In a Bayesian setting, traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of approaches for fitting statistical ABMs to data and demonstrate how to use multi-stage recursive Bayesian computing and statistical emulators to fit models in such a way that alleviates the need to have analytical knowledge of the ABM likelihood. Using two examples, a demographic model for survival and a compartment model for COVID-19, we illustrate statistical procedures for implementing ABMs. The approaches we describe are intuitive and accessible for practitioners and can be parallelised easily for additional computational efficiency.

Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning

Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.

Modeling asymptomatic infections and work-related human circulation as drivers of unstable malaria transmission in low-prevalence areas: A study in the Northern Peruvian Amazon

BACKGROUND

Despite relatively successful control campaigns, malaria remains a relevant public health problem in the Peruvian Amazon. Several studies suggest that malaria persistence in the area can be connected with a high prevalence of asymptomatic infections, which were subsequently shown to be connected with work-related exposure in areas of hyperendemic transmission. In this study, we tested the hypothesis that the infection reservoir represented by asymptomatic carriers in the northern Peruvian Amazon, combined with circular human movement to and from hyperendemic working areas, can capture the observed hypoendemic malaria transmission.

METHODS

We designed a set of agent-based models that represent local-scale malaria transmission in a typical riverine community in the northern Peruvian Amazon. The models include asymptomatic individuals as well as a full representation of human movements within the community and between the community and external hyperendemic working places. Several theoretical scenarios are explored to verify if and how malaria clinical immunity prevalence and human work-related movements influence the malaria morbidity registered in the community.

RESULTS

Agent-based simulations suggest that malaria incidence observed through passive case detection can be reproduced as exclusively generated by the asymptomatic infection reservoir. Scenarios analysis also show that, even if asymptomatic infections are completely eliminated, human movements to and from hyperendemic working areas generate a flow of imported cases that is enough to permit the persistence of transmission in the community. Simulation results were verified over a wide range of clinical immunity prevalence values and over a wide range of percentages of people working in remote hyperendemic areas. This context of unstable malaria transmission is observed to be vulnerable to severe outbreaks.

CONCLUSIONS

Asymptomatic malaria infection and occupational circular human movement to hyperendemic transmission areas are designated by agent-based models as possible exclusive causes of residual hypoendemic malaria transmission observed in the Peruvian Amazon. Control strategies are proposed to decrease asymptomatic infection prevalence and to block transmission from asymptomatic individuals to the malaria susceptible population.

Semi-field evaluation of the cumulative effects of a "Lethal House Lure" on malaria mosquito mortality

BACKGROUND

There is growing interest in the potential to modify houses to target mosquitoes with insecticides or repellents as they search for human hosts. One version of this 'Lethal House Lure' approach is the In2Care® EaveTube, which consists of a section of polyvinyl chloride (PVC) pipe fitted into a closed eave, with an insert comprising electrostatic netting treated with insecticide powder placed inside the tube. Preliminary evidence suggests that when combined with screening of doors and windows, there is a reduction in entry of mosquitoes and an increase in mortality. However, the rate of overnight mortality remains unclear. The current study used a field enclosure built around experimental huts to investigate the mortality of cohorts of mosquitoes over multiple nights.

METHODS

Anopheles gambiae sensu lato mosquitoes were collected from the field as larvae and reared through to adult. Three-to-five days old adult females were released inside an enclosure housing two modified West African style experimental huts at a field site in M'be, Côte d'Ivoire. Huts were either equipped with insecticide-treated tubes at eave height and had closed windows (treatment) or had open windows and open tubes (controls). The number of host-seeking mosquitoes entering the huts and cumulative mortality were monitored over 2 or 4 days.

RESULTS

Very few (0-0.4%) mosquitoes were able to enter huts fitted with insecticide-treated tubes and closed windows. In contrast, mosquitoes continually entered the control huts, with a cumulative mean of 50-80% over 2 to 4 days. Baseline mortality with control huts was approximately 2-4% per day, but the addition of insecticide-treated tubes increased mortality to around 25% per day. Overall cumulative mortality was estimated to be up to 87% over 4 days when huts were fitted with tubes.

CONCLUSION

Only 20-25% of mosquitoes contacted insecticide-treated tubes or entered control huts in a given night. However, mosquitoes continue to host search over sequential nights, and this can lead to high cumulative mortality over 2 to 4 days. This mortality should contribute to community-level reduction in transmission assuming sufficient coverage of the intervention.

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.

Mathematical modelling of the impact of expanding levels of malaria control interventions on Plasmodium vivax

Plasmodium vivax poses unique challenges for malaria control and elimination, notably the potential for relapses to maintain transmission in the face of drug-based treatment and vector control strategies. We developed an individual-based mathematical model of P. vivax transmission calibrated to epidemiological data from Papua New Guinea (PNG). In many settings in PNG, increasing bed net coverage is predicted to reduce transmission to less than 0.1% prevalence by light microscopy, however there is substantial risk of rebounds in transmission if interventions are removed prematurely. In several high transmission settings, model simulations predict that combinations of existing interventions are not sufficient to interrupt P. vivax transmission. This analysis highlights the potential options for the future of P. vivax control: maintaining existing public health gains by keeping transmission suppressed through indefinite distribution of interventions; or continued development of strategies based on existing and new interventions to push for further reduction and towards elimination.

Agent-based models of malaria transmission: a systematic review

BACKGROUND

Much of the extensive research regarding transmission of malaria is underpinned by mathematical modelling. Compartmental models, which focus on interactions and transitions between population strata, have been a mainstay of such modelling for more than a century. However, modellers are increasingly adopting agent-based approaches, which model hosts, vectors and/or their interactions on an individual level. One reason for the increasing popularity of such models is their potential to provide enhanced realism by allowing system-level behaviours to emerge as a consequence of accumulated individual-level interactions, as occurs in real populations.

METHODS

A systematic review of 90 articles published between 1998 and May 2018 was performed, characterizing agent-based models (ABMs) relevant to malaria transmission. The review provides an overview of approaches used to date, determines the advantages of these approaches, and proposes ideas for progressing the field.

RESULTS

The rationale for ABM use over other modelling approaches centres around three points: the need to accurately represent increased stochasticity in low-transmission settings; the benefits of high-resolution spatial simulations; and heterogeneities in drug and vaccine efficacies due to individual patient characteristics. The success of these approaches provides avenues for further exploration of agent-based techniques for modelling malaria transmission. Potential extensions include varying elimination strategies across spatial landscapes, extending the size of spatial models, incorporating human movement dynamics, and developing increasingly comprehensive parameter estimation and optimization techniques.

CONCLUSION

Collectively, the literature covers an extensive array of topics, including the full spectrum of transmission and intervention regimes. Bringing these elements together under a common framework may enhance knowledge of, and guide policies towards, malaria elimination. However, because of the diversity of available models, endorsing a standardized approach to ABM implementation may not be possible. Instead it is recommended that model frameworks be contextually appropriate and sufficiently described. One key recommendation is to develop enhanced parameter estimation and optimization techniques. Extensions of current techniques will provide the robust results required to enhance current elimination efforts.