Scale matters: Variations in spatial and temporal patterns of epidemic outbreaks in agent-based models

Agent-based modellers frequently make use of techniques to render simulated populations more computationally tractable on actionable timescales. Many generate a relatively small number of "representative” agents, each of which is "scaled up” to represent some larger number of individuals involved in the system being studied. The degree to which this "scaling” has implications for model forecasts is an underdeveloped field of study;in particular, there has been little known research on the spatial implications of such techniques. This work presents a case study of the impact of the simulated population size, using a model of the spread of COVID-19 among districts in Zimbabwe for the underlying system being studied. The impact of the relative scale of the population is explored in conjunction with the spatial setup, and crucial model parameters are varied to highlight where scaled down populations can be safely used and where modellers should be cautious. The results imply that in particular, different geographical dynamics of the spread of disease are associated with varying population sizes, with implications for researchers seeking to use scaled populations in their research. This article is an extension on work previously presented as part of the International Conference on Computational Science 2022 (Wise et al., 2022)[1]. © 2023 The Authors

Estimating spatially disaggregated probability of severe COVID-19 and the impact of hand washing interventions: the case of Zimbabwe

The severity of COVID-19 disease varies substantially between individuals, with some infections being asymptomatic while others are fatal. Several risk factors have been identified that affect the progression of SARS-CoV-2 to severe COVID-19. They include age, smoking and presence of underlying comorbidities such as respiratory illness, HIV, anemia and obesity. Given that respiratory illness is one such comorbidity and is affected by hand hygiene, it is plausible that improving access to hand washing could lower the risk of severe COVID-19 among a population. In this paper, we estimate the potential impact of improved access to hand washing on the risk of respiratory illness and its knock-on impact on the risk of developing severe COVID-19 disease across Zimbabwe. We use a geospatial model that allows us to estimate differential clinical risk at the district level. Results show that the current risk of severe disease is heterogeneous across the country, due to differences in individual characteristics and household conditions. This study demonstrates how household level improved access to handwashing could lead to reductions in the risk of severe COVID-19 of up to 16% from the estimated current levels across all districts. Taken alongside the likely impact on transmission of SARS-CoV-2 itself, as well as countless other pathogens, this result adds further support for the expansion of access to hand washing across the country. It also highlights the spatial differences in risk of severe COVID-19, and thus the opportunity for better planning to focus limited resources in high risk areas in order to potentially reduce the number of severe cases.

The Importance of Scaling for an Agent Based Model: An Illustrative Case Study with COVID-19 in Zimbabwe

Agent-based models frequently make use of scaling techniques to render the simulated samples of population more tractable. The degree to which this scaling has implications for model forecasts, however, has yet to be explored;in particular, no research on the spatial implications of this has been done. This work presents a simulation of the spread of Covid-19 among districts in Zimbabwe and assesses the extent to which results vary relative to the samples upon which they are based. It is determined that in particular, different geographical dynamics of the spread of disease are associated with varying population sizes, with implications for others seeking to use scaled populations in their research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Assessing Bias in Smartphone Mobility Estimates in Low Income Countries

It has become common for governments and practitioners to measure mobility using data from smartphones, especially during the COVID-19 pandemic. Yet in countries where few people have smartphones, or use mobile internet, the movement of smartphones may not be a good indicator of the movement of the population. This paper develops a framework for approaching potential bias that can arise when measuring mobility with smartphones. Using mobile phone operator records in Uganda, we compare the mobility of smartphones and the basic and feature phones that are more common. Smartphones have different travel patterns, and decrease mobility substantially more in response to a COVID-19 lockdown. This suggests caution when interpreting smartphone mobility estimates in contexts with low adoption. © 2021 ACM.