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]

Climate Anomalies and International Migration: A Disaggregated Analysis for West Africa

West Africa is vulnerable to negative impacts of climate change and a potential channel of adjustment is migration. Using novel geo-referenced and high-frequency data, we investigate the extent to which soil moisture anomalies have an impact on international migration within the region and directed to Europe. Our findings show that drier soil conditions decrease rather than increase the probability to migrate. A standard deviation decrease in soil moisture leads to a 2 percentage points drop in the probability to migrate, which is equivalent to a decrease of about 25% in the number of migrants. This effect is concentrated during the crop-growing season, suggesting that the decrease in migration is mainly driven by financial constraints. The effect is only seen for areas that are in the middle of the income distribution, with no impact on the poorest or richest areas of a country

Climate anomalies and international migration: A disaggregated analysis for West Africa

Migration is one of the channels West African populations can use to adjust to the negative impacts of climate change. Using novel geo-referenced and high-frequency data, this study investigates the extent to which soil moisture anomalies drive international migration decisions within the region and toward Europe. The findings show that drier soil conditions decrease (rather than increase) the probability to migrate. A standard deviation decrease in soil moisture leads to a 2 percentage point drop in the probability to migrate, equivalent to a 25 percent decrease in the number of migrants. This effect is concentrated during the crop-growing season, and likely driven by financial constraints. The effect is only seen for areas that are in the middle of the income distribution, with no impact on the poorest or richest areas of a country, suggesting that the former were constrained to start and the latter can address those financial constraints.Migration ist eine der Möglichkeiten, welche die westafrikanische Bevölkerung nutzen kann, um sich an die negativen Auswirkungen des Klimawandels anzupassen. Unter Verwendung neuartiger georeferenzierter und hochfrequenter Daten wird in dieser Studie untersucht, inwieweit Anomalien der Bodenfeuchte internationale Migrationsentscheidungen innerhalb der Region und in Richtung Europa beeinflussen. Die Ergebnisse zeigen, dass trockenere Bodenbedingungen die Migrationswahrscheinlichkeit verringern (anstatt zu erhöhen). Eine Abnahme der Standardabweichung der Bodenfeuchte resultiert in einem Rückgang der Migrationswahrscheinlichkeit um 2 Prozentpunkte, was einem Rückgang der Anzahl der Migranten um 25 Prozent entspricht. Dieser Effekt konzentriert sich auf die Anbausaison und wird wahrscheinlich durch finanzielle Einschränkungen verursacht. Der Effekt ist nur für Gebiete beobachtbar, die in der Mitte der Einkommensverteilung liegen, ohne Auswirkung auf die ärmsten oder reichsten Gebiete eines Landes, was darauf hindeutet, dass erstere zu Beginn eingeschränkt waren und letztere die finanziellen Beschränkungen ausgleichen können

Challenges and opportunities in accessing mobile phone data for COVID-19 response in developing countries

Anonymous and aggregated statistics derived from mobile phone data have proven efficacy as a proxy for human mobility in international development work and as inputs to epidemiological modeling of the spread of infectious diseases such as COVID-19. Despite the widely accepted promise of such data for better development outcomes, challenges persist in their systematic use across countries. This is not only the case for steady-state development use cases such as in the transport or urban development sectors, but also for sudden-onset emergencies such as epidemics in the health sector or natural disasters in the environment sector. This article documents an effort to gain systematized access to and use of anonymized, aggregated mobile phone data across 41 countries, leading to fruitful collaborations in nine developing countries over the course of one year. The research identifies recurring roadblocks and replicable successes, offers lessons learned, and calls for a bold vision for future successes. An emerging model for a future that enables steady-state access to insights derived from mobile big data - such that they are available over time for development use cases - will require investments in coalition building across multiple stakeholders, including local researchers and organizations, awareness raising of various key players, demand generation and capacity building, creation and adoption of standards to facilitate access to data and their ethical use, an enabling regulatory environment and long-term financing schemes to fund these activities

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012-2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network (<1%) where 50% of the crashes identified occurred. Even with limitations in the representativeness of the data, the results can provide urban planners with useful information that can be used to target road safety improvements where resources are limited. The work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments

A stitch in time: The importance of water and sanitation services (WSS) infrastructure maintenance for cholera risk. A geospatial analysis in Harare, Zimbabwe.

Understanding the factors associated with cholera outbreaks is an integral part of designing better approaches to mitigate their impact. Using a rich set of georeferenced case data from the cholera epidemic that occurred in Harare from September 2018 to January 2019, we apply spatio-temporal modelling to better understand how the outbreak unfolded and the factors associated with higher risk of being a reported case. Using Call Detail Records (CDR) to estimate weekly population movement of the community throughout the city, results suggest that broader human movement (not limited to infected agents) helps to explain some of the spatio-temporal patterns of cases observed. In addition, results highlight a number of socio-demographic risk factors and suggest that there is a relationship between cholera risk and water infrastructure. The analysis shows that populations living close to the sewer network, with high access to piped water are associated with at higher risk. One possible explanation for this observation is that sewer bursts led to the contamination of the piped water network. This could have turned access to piped water, usually assumed to be associated with reduced cholera risk, into a risk factor itself. Such events highlight the importance of maintenance in the provision of SDG improved water and sanitation infrastructure