COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

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Survey research in the Global South traditionally requires large budgets and lengthy fieldwork, for which researchers hire local enumerators to conduct face-to-face surveys with respondents. However, much of the world’s population is now digitally accessible, offering an opportunity for researchers with limited budgets and those seeking to study settings where in-person contact is impossible, such as natural disasters, violent conflicts, and pandemics. In this paper, we evaluate whether Facebook advertising can be used to cost-effectively generate representative survey samples in the Global South. We introduce a framework for evaluating quality in Facebook survey samples, highlighting key trade-offs for researchers considering the platform. We then quota-sample respondents in two countries: Mexico (n=5,168) and Kenya (n=1,452) to evaluate how well these samples perform on a diverse set of survey indicators related to both internal and external validity. We find that while the Facebook platform can quickly and cheaply recruit respondents, these samples tend to be more male, more educated, and more urban than the overall national populations. Applying post-stratification weighting after oversampling key demographic variables ameliorates, but does not fully overcome, these initial sample imbalances. Our analysis demonstrates the considerable potential of Facebook advertisements to cost-effectively conduct research in diverse global settings.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable.