Artificial intelligence and machine learning in clinical trial design and application

In 2020, COVID-19 changed how health care was approached both in the United States and globally. In the early phases, the vast majority of energy and attention was devoted to containing the pandemic and treating the infected. Toward the end of 2020, that attention expanded to vaccinating people across the globe. What was not being considered at the time were challenges to future health and clinical trials that power new treatments for COVID-19 and non-COVID-19 treatments. © 2023 Elsevier Inc. All rights reserved.

Assessing the effectiveness of mandatory outdoor mask policy: The natural experiment of Campania.

Face masks are possibly the main symbol of the COVID-19 pandemic. Once rarely used in Western countries, in the last two years they have become an object it is impossible to leave one's home without. Italy made their use a legal requirement, even outdoors, from late 2020 to early 2022. The effectiveness of this policy in reducing COVID-19 cases has been widely debated. The recent cancellation of their mandatory use in Italy offers an interesting setting in which to test its impact, since one Italian region (Campania) extended the restriction for a further three weeks. We aim to shed some light on the real-world impact of mandatory use of face masks outdoors, identifying the effect of this policy on the spread of COVID-19. By means of a quantitative analysis, employing a synthetic control method approach, we find that Campania had statistically the same number of cases as its synthetic counterfactual, built from a donor pool formed from the other Italian provinces. Hence, results suggest that while it imposes a burden on the public, the use of face masks outdoors is not correlated with a decrease in the number of COVID-19 cases.

Understanding the globalization-crisis linkage: A "differenced” approach

The recent string of adverse global shocks (financial crisis, trade war, COVID-19, Ukraine war) poses a potential challenge to the well-known welfare enhancing effects of globalization, necessitating a better understanding of the longer run globalization-crisis linkage as opposed to its shorter run effects. Focusing on the Great Recession, we discover an evolving role of trade and financial openness from one that propagates and deepens the negative effects of crises to one that confirms its well-established contributions. Key to this is generating counterfactual output for open countries as if they were closed and examining the comparative impact of the crisis. © 2023 The Authors. Review of International Economics published by John Wiley & Sons Ltd.

Sudden lockdown repeals, social mobility, and COVID-19: Evidence from a judicial natural experiment

The imposition and lifting of COVID-19 lockdown orders were among the most heatedly debated policies during the pandemic. Credible empirical evaluations of the effects of reopening policies are difficult because policymakers often explicitly linked sustained reductions in COVID-19 cases to the lifting of lockdown orders. This hardwired policy endogeneity creates challenges in isolating the causal effects of lifting of lockdown orders on social mobility and public health. To overcome simultaneity bias, we exploit a natural experiment generated by the Wisconsin Supreme Court when it abolished Wisconsin's "Safer at Home” order on separation-of-powers grounds. We capitalize on this sudden, dramatic, and largely unanticipated termination of a statewide lockdown order to estimate its effect—relative to a more gradual scaling back of restrictions—on social mobility and COVID-19 case growth. First, using anonymized smartphone data from SafeGraph and a synthetic control design, we find that termination of COVID-related restrictions had small and short-lived negative impacts on social distancing. Then, using data on case and mortality rates, we find no evidence that the Wisconsin Supreme Court decision impacted COVID-19 growth up to a month following the repeal. These findings suggest that in the absence of carrying new information, sudden lockdown repeals may generate smaller behavioral responses than policymakers anticipate. © 2023 The Authors. Journal of Empirical Legal Studies published by Cornell Law School and Wiley Periodicals LLC.

Operations (management) warp speed: Rapid deployment of hospital‐focused predictive/prescriptive analytics for the COVID‐19 pandemic

At the onset of the COVID‐19 pandemic, hospitals were in dire need of data‐driven analytics to provide support for critical, expensive, and complex decisions. Yet, the majority of analytics being developed were targeted at state‐ and national‐level policy decisions, with little availability of actionable information to support tactical and operational decision‐making and execution at the hospital level. To fill this gap, we developed a multi‐method framework leveraging a parsimonious design philosophy that allows for rapid deployment of high‐impact predictive and prescriptive analytics in a time‐sensitive, dynamic, data‐limited environment, such as a novel pandemic. The product of this research is a workload prediction and decision support tool to provide mission‐critical, actionable information for individual hospitals. Our framework forecasts time‐varying patient workload and demand for critical resources by integrating disease progression models, tailored to data availability during different stages of the pandemic, with a stochastic network model of patient movements among units within individual hospitals. Both components employ adaptive tuning to account for hospital‐dependent, time‐varying parameters that provide consistently accurate predictions by dynamically learning the impact of latent changes in system dynamics. Our decision support system is designed to be portable and easily implementable across hospital data systems for expeditious expansion and deployment. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana, which has 18 hospitals serving over one million residents. Our initial prototype was implemented in April 2020 and has supported managerial decisions, from the operational to the strategic, across multiple functionalities at IU Health.

Children, vaccines, and financial incentives.

Recent studies have been analyzing and measuring the efficacy of the use of financial incentives to increase the Covid-19 vaccine uptake. To the best of our knowledge, this paper is the only study available in the literature that aims to measure the effect of financial incentives on vaccine rates among children. This paper explores the effects of a specific financial incentive on parents' vaccination decisions for their children. Using data from a regional practice, where students aged 12 and older received $50 gift cards per Covid-19 vaccination dose, we use various methodologies (synthetic control, linear regression, and difference-in-differences) to approximate the effects of financial incentives on vaccine rates. Our analysis reveals that gift cards increase vaccination rates by 2.64-4.23 percentage points from a baseline rate of 38 percent, concluding that financial incentives, in conjunction with other incentives and policies, can be considered to increase the rate of vaccines for 12- to 17-year-olds.

Effects of the Pelotas (Brazil) Peace Pact on violence and crime: a synthetic control analysis.

Background: City-led interventions are increasingly advocated to achieve the UN's Sustainable Development Goal to reduce violence for all. We used a new quantitative evaluation method to examine whether a flagship programme, called the "Pelotas Pact for Peace" (the Pacto), has been effective in reducing violence and crime in the city of Pelotas, Brazil. Methods: We used synthetic control methodology to assess the effects of the Pacto from August 2017 to December 2021, and separately before and during the COVID-19 pandemic. Outcomes included monthly rates of homicide and property crime, and yearly rates of assault against women and school drop-out. We constructed synthetic controls (counterfactuals) based on weighted averages from a donor pool of municipalities in Rio Grande do Sul. Weights were identified using pre-intervention outcome trends and confounders (sociodemographics, economics, education, health and development, and drug trafficking). Findings: The Pacto led to an overall 9% reduction in homicide and 7% reduction in robbery in Pelotas. These effects were not uniform across the full post-intervention period as clear effects were only seen during the pandemic period. A 38% reduction in homicide was also specifically associated with the criminal justice strategy of Focussed Deterrence. No significant effects were found for non-violent property crimes, violence against women, and school dropout, irrespective of the post-intervention period. Interpretation: City-level interventions that combine public health and criminal justice approaches could be effective in tackling violence in Brazil. Continued monitoring and evaluation efforts are increasingly needed as cities are proposed as key opportunities for reducing violence for all. Funding: This research was funded by the Wellcome Trust [grant number: 210735_Z_18_Z].

A synthetic control analysis of U.S. state level COVID-19 stay-at-home orders on new jobless claims

There is an ongoing debate regarding the economic consequences of public policies designed to curb public health crises, such as the COVID-19 pandemic. Many opponents of such policies claim that their economic costs may outweigh their health benefits. In this paper, we use synthetic control analysis to determine the impact of stay-at-home orders on weekly new jobless claims during the initial phase of the COVID-19 pandemic. Our analysis reveals that while new jobless claims spike following the stay-at-home orders, similar spikes are observed within our synthetic control. Specifically, we find that stay-at-home orders account for only 32 percent of the increase in new jobless claims, with the majority of the increase being driven by factors outside of the policy, such as the general spread of the virus and waning consumer confidence. © 2022, Academy of Economics and Finance.

The effect of a nighttime curfew on the spread of COVID-19.

While nighttime curfews are less severe restrictions compared to around-the-clock curfews in mitigating the spread of Covid-19, they are nevertheless highly controversial, with the scarce literature on their effectiveness providing mixed evidence. We study the effectiveness of the nighttime curfew in Hamburg, Germany's second largest city, in mitigating the spread of Covid-19. This curfew forbid people from leaving their home between 9 p.m. and 5 a.m. for non-essential businesses. Applying both difference-in-differences and synthetic control methods, we find that the curfew was effective in reducing the number of Covid-19 cases. As it is unclear whether and how the virus will mutate in the next time, policy-makers might have to resort to non-pharmaceutical interventions again. Nighttime curfews should be kept in the toolbox of policy-makers to fight Covid-19.

Effects of Covid-19 lockdowns on social distancing in Turkey

This paper elucidates the causal effect of lockdowns on social distancing behaviour in Turkey by adopting an augmented synthetic control and a factor-augmented model approach for imputing counterfactuals. By constructing a synthetic control group that reproduces pre-lockdown trajectory of mobility of the treated provinces and that accommodates staggered adoption, the difference between the counterfactual and actual mobility of treated provinces is assessed in the post-lockdown period. The analysis shows that in the short run following the onset of lockdowns, outdoor mobility would have been about 17-53 percentage points higher on average in the absence of lockdowns, depending on social distancing measure. However, residential mobility would have been about 12 percentage points lower in the absence of lockdowns. The findings are corroborated using interactive fixed effects and matrix completion counterfactuals that accommodate staggered adoption and treatment reversals.

Quantifying the impact of the Tokyo Olympics on COVID-19 cases using synthetic control methods.

This paper uses a synthetic control method (SCM) and a Ridge Augmented SCM to estimate the impact of holding the Tokyo Olympic games on the number of newly confirmed COVID-19 cases in Tokyo (Japan). Our analysis with these methods enables us to estimate the causal impact of the Tokyo Olympics on COVID-19 cases by constructing counterfactual COVID-19 cases for Tokyo (Japan) as the optimal weighted average of COVID-19 cases of OECD countries that are not affected by holding the Olympics through a data-driven approach. Based on reliable estimates obtained from different analytical settings, we find that, compared to the counterfactuals, holding the Tokyo Olympics significantly increased the daily average number of COVID-19 cases by 105 to 132 cases in Tokyo (47 to 65 cases in Japan as a whole) per million people. This result suggests that holding the Olympics likely led to the spread of COVID-19 infection in Tokyo (Japan).

Economic resilience in times of public health shock: The case of the US states.

Does adopting social distancing policies amid a health crisis, e.g., COVID-19, hurt economies? Using a machine learning approach at the intermediate stage, we applied a generalized synthetic control method to answer this question. We utilize state policy response differences. Cross-validation, a machine learning approach, is used to produce the "counterfactual" for adopting states-how they "would have behaved" without lockdown orders. We categorize states with social distancing as the treatment group and those without as the control. We employ the state time-period for fixed effects, adjusting for selection bias and endogeneity. We find significant and intuitively explicable impacts on some states, such as West Virginia, but none at the aggregate level, suggesting that social distancing may not affect the entire economy. Our work implies a resilience index utilizing the magnitude and significance of the social distancing measures to rank the states' resilience. These findings help governments and businesses better prepare for shocks.

The Impact of COVID-19 on Online Games: Machine Learning and Difference-in-Difference

By intervening in people’s behavior, governments in several nations have established a variety of strategies to slow down the spread of COVID-19 pandemic. At the same time, it has a different impact on everyone. Data from the Steam platform online games between January 2018 and February 2021 was used for this project’s analysis. Through the difference-in-difference model in Synthetic Control Methods to quantify and analyze, crucial positive effect on Steam’s online players during COVID-19 and the increase of the number of online players and the released games of the platform in 2020 had been found. The machine learning prediction model was created using the daily totals of the online gaming players of the most popular games on the site. The Ridge regression, whose R squared reached 0.805, had been demonstrated by the experimental results that it got the best performance. Simultaneously, this work found the features of the COVID-19 pandemic and the features of the human mobility, which helps to build a great majority of the predictive models. © 2022, Springer Nature Singapore Pte Ltd.

Terminated local subsidy on electric vehicle adoption during the COVID-19 pandemic: The case of Chongqing City.

Chongqing, one of the four municipalities directly under the Central Government in China, terminated local subsidies for electric vehicles (EVs) on June 26, 2019. Shortly after the termination, EV adoption in China was affected by the coronavirus disease (COVID-19) pandemic. However, little research studies on whether the terminated local subsidy has a lasting impact on EV adoption, especially during the pandemic. Using EV adoption data from Chongqing and 44 other cities in China, this study aims to fill this gap by first proposing a new method to estimate the unobservable data of the treated unit in the preintervention periods to obtain accurate results. This study then also estimates unobservable data for more conservative results. The findings show that the terminated subsidy has had a significant positive impact on EV adoption during the COVID-19 pandemic compared to the situation where a local subsidy was never provided. The results show that in Chongqing, during the first five months of the pandemic, terminated local subsidy helped reduce the loss of EV adoption by approximately 3141 units when accurately estimated, and approximately 1696 units when more conservatively estimated. These findings help to understand the role of subsidies both during implementation and after their termination.

The limits of reopening policy to alter economic behavior: New evidence from Texas.

In the midst of mass COVID-19 vaccination distribution efforts in the U.S. Texas became the first state to abolish its mask mandate and fully lift capacity constraints for all businesses, effective on March 10, 2021. Proponents claimed that the reopening would generate short-run employment growth and signal a return to normal while opponents argued that it would cause a resurgence of COVID-19 and kill Texans. This study finds that each side was largely incorrect. First, using daily anonymized smartphone data - and synthetic control and difference-in-differences approaches - we find no evidence that the Texas reopening led to substantial changes in mobility, including foot traffic at a wide set of business establishments. Second, we find no evidence that the Texas reopening affected the rate of new COVID-19 cases or deaths during the five weeks following the reopening. Our null results persist across more urbanized and less urbanized counties, as well as across counties that supported Donald Trump and Joe Biden in the 2020 presidential election. Finally, we find no evidence that the Texas reopening impacted short-run employment, including in industries most affected by the reopening. Together, these findings underscore the persistence of late-pandemic era private behavior and stickiness in individuals' risk-related beliefs, and suggest that reopening policies may have impacts that are more muted than policymakers expect. Supplementary information: The online version contains supplementary material available at 10.1007/s11166-022-09379-8.

THE OHIO VACCINE LOTTERY AND STARTING VACCINATION RATES

We find that Ohio’s “Vax-a-Million” lottery increased first-dose COVID-19 vaccinations by between 50,000 and 100,000, with most of the additional doses occurring during the two weeks between the announcement and the first lottery drawing. We use county-level data and two empirical approaches to provide causal estimates of the lottery in Ohio. First, a difference-in-differences design compares vaccination rates in border counties in Ohio and Indiana before and after the announcement. Second, we use a pooled synthetic control method to construct a counterfactual for each of Ohio’s counties using control counties in Indiana, Michigan, and Pennsylvania. The synthetic control analysis reveals larger increases in vaccination rates in more populous counties. Our estimates imply that Ohio paid about $75 per additional starting dose during this period. © 2022 American Society of Health Economists.

Observational Study of the Effect of the Juvenile Stay-At-Home Order on SARS-CoV-2 Infection Spread in Saline County, Arkansas

We assess the treatment effect of juvenile stay-at-home orders (JSAHO) on reducing the rate of SARS-CoV-2 infection spread in Saline County ("Saline"), Arkansas, by examining the difference between Saline's and control Arkansas counties' changes in daily and mean log infection rates of pretreatment (March 28-April 5, 2020) and treatment periods (April 6-May 6, 2020). A synthetic control county is constructed based on the parallel-trends assumption, least-squares fitting on pretreatment and socio-demographic covariates, and elastic-net-based methods, from which the counterfactual outcome is predicted and the treatment effect is estimated using the difference-in-differences, the synthetic control, and the changes-in-changes methodologies. Both the daily and average treatment effects of JSAHO are shown to be significant. Despite its narrow scope and lack of enforcement for compliance, JSAHO reduced the rate of the infection spread in Saline. Supplementary materials for this article are available online.

Evaluating the effectiveness of Hong Kong's border restriction policy in reducing COVID-19 infections.

This study evaluates the effectiveness of Hong Kong's strict border restrictions with mainland China in curbing the transmission of COVID-19. Combining big data from Baidu Population Migration with traditional meteorological data and census data for over 200 Chinese cities, we utilize an advanced quantitative approach, namely synthetic control modeling, to produce a counterfactual "synthetic Hong Kong" without a strict border restriction policy. We then simulate infection trends under the hypothetical scenarios and compare them to actual infection numbers. Our counterfactual synthetic control model demonstrates a lower number of COVID-19 infections than the actual scenario, where strict border restrictions with mainland China were implemented from February 8 to March 6, 2020. Moreover, the second synthetic control model, which assumes a border reopen on 7 May 2020 demonstrates nonpositive effects of extending the border restriction policy on preventing and controlling infections. We conclude that the border restriction policy and its further extension may not be useful in containing the spread of COVID-19 when the virus is already circulating in the local community. Given the substantial economic and social costs, and as precautionary measures against COVID-19 becomes the new normal, countries can consider reopening borders with neighbors who have COVID-19 under control. Governments also need to closely monitor the changing epidemic situations in other countries in order to make prompt and sensible amendments to their border restriction policies.

Exploring the Impact of the US’s Lottery Incentives on COVID-19 Vaccination Rates

Many states in the US have carried out lotteries with tantalizing prizes to reduce the covid-19 vaccine hesitancy. However, there has yet been a consensus regarding the effectiveness of such incentives. This study conducts a synthetic control analysis for each treated state, to provide a better understanding of the influence that the lottery programs have made on the vaccination rates across different states. However, for all treated states, no evidence is found for the effects of the lotteries. Next, the article investigates the impact of people's policy ideology on the prediction of the vaccination rates under the synthetic control models. Within each treated state, counties are categorized into two regions according to their political affiliation. The comparison of the treatment effects between the two regions indicates that there is no relationship between people's policy ideology and the vaccination rates. © 2021 ACM.

Public health implications of opening National Football League stadiums during the COVID-19 pandemic.

SignificanceUsing data from 2020, we measure the public health impact of allowing fans into sports stadiums during the COVID-19 pandemic; these results may inform future policy decisions regarding large outdoor gatherings during public health crises. Second, we demonstrate the utility of robust synthetic control in this context. Synthetic control and other statistical approaches may be used to exploit the underlying low-dimensional structure of the COVID-19 data and serve as useful instruments in analyzing the impact of mitigation strategies adopted by different communities. As with all statistical methods, reliable outcomes depend on proper implementation strategies and well-established robustness tests; in the absence of these safeguards, these statistical methods are likely to produce specious or misleading conclusions.