The Economic Case for a Pandemic Fund

COVID-19—What Price Do Children Pay? An Analysis of Economic and Social Policy Factors

Numerous studies have addressed the indirect consequences of the COVID-19 pandemic for children such as social isolation or increases in reported child maltreatment. Research on the economic and sociopolitical consequences is scarce as they can only be evaluated with a time lag. To improve our understanding of future, long-term developments in the context of the COVID-19 pandemic, we gathered findings from the still unexploited empirical literature on the aftermath of earlier pandemics, epidemics, and other infectious disease outbreaks. On top of this, we scrutinized research on past economic crises to interpret the link between changes in the economy and the health of children. Many of the side effects of battling the spread of the current pandemic, such as school closures, the stigma of infection, or conflicts about vaccines, are not novel and have already been documented in connection with previous infectious disease outbreaks. Results highlight that changes in the financial situation of families and socio-political challenges affect the situation and daily routine of children and youth in the long term. In consequence, the already pronounced socioeconomic inequalities will likely further increase. On top of this, due to reduced revenues, child protective services are likely to face challenges in the availability of human and financial resources.

Challenges of integrating economics into epidemiological analysis of and policy responses to emerging infectious diseases

COVID-19 has shown that the consequences of a pandemic are wider-reaching than cases and deaths. Morbidity and mortality are important direct costs, but infectious diseases generate other direct and indirect benefits and costs as the economy responds to these shocks: some people lose, others gain and people modify their behaviours in ways that redistribute these benefits and costs. These additional effects feedback on health outcomes to create a complicated interdependent system of health and non-health outcomes. As a result, interventions primarily intended to reduce the burden of disease can have wider societal and economic effects and more complicated and unintended, but possibly not anticipable, system-level influences on the epidemiological dynamics themselves. Capturing these effects requires a systems approach that encompasses more direct health outcomes. Towards this end, in this article we discuss the importance of integrating epidemiology and economic models, setting out the key challenges which such a merging of epidemiology and economics presents. We conclude that understanding people's behaviour in the context of interventions is key to developing a more complete and integrated economic-epidemiological approach; and a wider perspective on the benefits and costs of interventions (and who these fall upon) will help society better understand how to respond to future pandemics.

Investing to Both Prevent and Prepare for COVID-XX

One cause of the high rate of COVID-19 cases in the USA is thought to be insufficient prior capital investment in national health programs to preemptively reduce the likelihood of an outbreak and in national capacity to reduce the severity of any outbreak that does occur. We analyze the choice of capital investments (e.g. testing capacity, stockpiles of PPE, and information sharing capacity) and find the economically efficient capital stock associated with mitigating pandemic risk should be dramatically expanded. Policymakers who fail to invest in public health forgo significant expected cost savings from being prepared.

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

The socioeconomic and environmental drivers of the COVID-19 pandemic: A review

In recent decades, there has been an intensification of the socioeconomic and environmental drivers of pandemics, including ecosystem conversion, meat consumption, urbanization, and connectivity among cities and countries. This paper reviews how these four systemic drivers help explain the dynamics of the COVID-19 pandemic and other recent emerging infectious diseases, and the policies that can be adopted to mitigate their risks. Land-use change and meat consumption increase the likelihood of pathogen spillover from animals to people. The risk that such zoonotic outbreaks will then spread to become pandemics is magnified by growing urban populations and the networks of trade and travel within and among countries. Zoonotic spillover can be mitigated through habitat protection and restrictions on the wildlife trade. Containing infectious disease spread requires a high degree of coordination among institutions across geographic jurisdictions and economic sectors, all backed by international investment and cooperation.

Control and prevention of infectious diseases from a One Health perspective

The ongoing COVID-19 pandemic has caught the attention of the global community and rekindled the debate about our ability to prevent and manage outbreaks, epidemics, and pandemics. Many alternatives are suggested to address these urgent issues. Some of them are quite interesting, but with little practical application in the short or medium term. To realistically control infectious diseases, human, animal, and environmental factors need to be considered together, based on the One Health perspective. In this article, we highlight the most effective initiatives for the control and prevention of infectious diseases: vaccination; environmental sanitation; vector control; social programs that encourage a reduction in the population growth; control of urbanization; safe sex stimulation; testing; treatment of sexually and vertically transmitted infections; promotion of personal hygiene practices; food safety and proper nutrition; reduction of the human contact with wildlife and livestock; reduction of social inequalities; infectious disease surveillance; and biodiversity preservation. Subsequently, this article highlights the impacts of human genetics on susceptibility to infections and disease progression, using the SARS-CoV-2 infection as a study model. Finally, actions focused on mitigation of outbreaks and epidemics and the importance of conservation of ecosystems and translational ecology as public health strategies are also discussed.

Africa: The livestock revolution urbanizes

Population growth, rising income and urbanization have fueled a significant increase in demand for animal products in developing countries since the early 1970s. The phenomenon, dubbed as the Livestock Revolution, is anticipated to slow down in the coming decades, except in Africa where the Revolution is expected to continue and urbanize. This paper examines the urbanization of the Livestock Revolution in Africa. It estimates that in 2050 almost 70% of total meat and milk consumption will likely come from cities, with urban dwellers demanding, compared to today, 28 and 47 additional million metric tons of meat and milk, respectively. The consequent transformations of the livestock value chain serving urban and peri-urban areas may pose unprecedented public health and environmental challenges to policy-makers.

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Between 2015 and 2050, meat and milk demand should respectively triple and double in Africa.

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In 2050, almost 70% of meat and milk demand in Africa should come from urban areas.

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The number of mid- and large-scale livestock operators in peri-urban areas will likely increase.

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Urban and peri-urban areas could be veritable hotspots for zoonotic diseases.