Emerging properties of malaria transmission and persistence in urban Accra, Ghana: evidence from a participatory system approach.

BACKGROUND: Several studies that aim to enhance the understanding of malaria transmission and persistence in urban settings failed to address its underlining complexity. This study aims at doing that by applying qualitative and participatory-based system analysis and mapping to elicit the system's emergent properties. METHODS: In two experts' workshops, the system was sketched and refined. This system was represented through a causal loop diagram, where the identification of leverage points was done using network analysis. RESULTS: 45 determinants interplaying through 56 linkages, and three subsystems: urbanization-related transmission, infection-prone behaviour and healthcare efficiency, and Plasmodium resistance were identified. Apart from the number of breeding sites and malaria-positive cases, other determinants such as drug prescription and the awareness of householders were identified by the network analysis as leverage points and emergent properties of the system of transmission and persistence of malaria. CONCLUSION: Based on the findings, the ongoing efforts to control malaria, such as the use of insecticide-treated bed nets and larvicide applications should continue, and new ones focusing on the public awareness and malaria literacy of city dwellers should be included. The participatory approach strengthened the legitimacy of the recommendations and the co-learning of participants.

Urbanization and malaria have a contextual relationship in endemic areas: A temporal and spatial study in Ghana.

In West Africa, malaria is one of the leading causes of disease-induced deaths. Existing studies indicate that as urbanization increases, there is corresponding decrease in malaria prevalence. However, in malaria-endemic areas, the prevalence in some rural areas is sometimes lower than in some peri-urban and urban areas. Therefore, the relationship between the degree of urbanization, the impact of living in urban areas, and the prevalence of malaria remains unclear. This study explores this association in Ghana, using epidemiological data at the district level (2015-2018) and data on health, hygiene, and education. We applied a multilevel model and time series decomposition to understand the epidemiological pattern of malaria in Ghana. Then we classified the districts of Ghana into rural, peri-urban, and urban areas using administratively defined urbanization, total built areas, and built intensity. We converted the prevalence time series into cross-sectional data for each district by extracting features from the data. To predict the determinant most impacting according to the degree of urbanization, we used a cluster-specific random forest. We find that prevalence is impacted by seasonality, but the trend of the seasonal signature is not noticeable in urban and peri-urban areas. While urban districts have a slightly lower prevalence, there are still pockets with higher rates within these regions. These areas of high prevalence are linked to proximity to water bodies and waterways, but the rise in these same variables is not associated with the increase of prevalence in peri-urban areas. The increase in nightlight reflectance in rural areas is associated with an increased prevalence. We conclude that urbanization is not the main factor driving the decline in malaria. However, the data indicate that understanding and managing malaria prevalence in urbanization will necessitate a focus on these contextual factors. Finally, we design an interactive tool, 'malDecision' that allows data-supported decision-making.

A standardised differential privacy framework for epidemiological modeling with mobile phone data.

During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework.

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.

One Health in the context of coronavirus outbreaks: A systematic literature review.

The ongoing coronavirus disease 2019 (COVID-19) pandemic threatens global health thereby causing unprecedented social, economic, and political disruptions. One way to prevent such a pandemic is through interventions at the human-animal-environment interface by using an integrated One Health (OH) approach. This systematic literature review documented the three coronavirus outbreaks, i.e. SARS, MERS, COVID-19, to evaluate the evolution of the OH approach, including the identification of key OH actions taken for prevention, response, and control. The OH understandings identified were categorized into three distinct patterns: institutional coordination and collaboration, OH in action/implementation, and extended OH (i.e. a clear involvement of the environmental domain). Across all studies, OH was most often framed as OH in action/implementation and least often in its extended meaning. Utilizing OH as institutional coordination and collaboration and the extended OH both increased over time. OH actions were classified into twelve sub-groups and further categorized as classical OH actions (i.e. at the human-animal interface), classical OH actions with outcomes to the environment, and extended OH actions. The majority of studies focused on human-animal interaction, giving less attention to the natural and built environment. Different understandings of the OH approach in practice and several practical limitations might hinder current efforts to achieve the operationalization of OH by combining institutional coordination and collaboration with specific OH actions. The actions identified here are a valuable starting point for evaluating the stage of OH development in different settings. This study showed that by moving beyond the classical OH approach and its actions towards a more extended understanding, OH can unfold its entire capacity thereby improving preparedness and mitigating the impacts of the next outbreak.