Are We #StayingHome to Flatten the Curve?

The recent spread of COVID-19 across the U.S. led to concerted efforts by states to ``flatten the curve" through the adoption of stay-at-home mandates that encourage individuals to reduce travel and maintain social distance. Combining data on changes in travel activity with COVID-19 health outcomes and state policy adoption timing, we characterize nationwide changes in mobility patterns and isolate the portion attributable to statewide mandates. We find evidence of dramatic nationwide declines in mobility prior to adoption of any statewide mandates. Once states adopt a mandate, we estimate further mandate-induced declines between 2.1 and 7.0 percentage points across methods that account for states' differences in travel behavior prior to policy adoption. In addition, we investigate the effects of stay-at-home mandates on changes in COVID-19 health outcomes while controlling for pre-trends and observed pre-treatment mobility patterns. We estimate mandate-induced declines between 0.13 and 0.17 in deaths (5.6 to 6.0 in hospitalizations) per 100 thousand across methods. Across 43 adopting states, this represents 23,366-30,144 fewer deaths (and roughly one million averted hospitalizations) for the months of March and April - which indicates that death rates could have been 42-54% higher had states not adopted statewide policies. We further find evidence that changes in mobility patterns prior to adoption of statewide policies also played a role in reducing COVID-19 mortality and morbidity. Adding in averted deaths due to pre-mandate social distancing behavior, we estimate a total of 48-71,000 averted deaths from COVID-19 for the two-month period. Given that the actual COVID-19 death toll for March and April was 55,922, our estimates suggest that deaths would have been 1.86-2.27 times what they were absent any stay-at-home mandates during this period. These estimates represent a lower bound on the health impacts of stay-at-home policies, as they do not account for spillovers or undercounting of COVID-19 mortality. Our findings indicate that early behavior changes and later statewide policies reduced death rates and helped attenuate the negative consequences of COVID-19. Further, our findings of substantial reductions in mobility prior to state-level policies convey important policy implications for re-opening.Take Away Link https://are.berkeley.edu/sites/are.berkeley.edu/files/PolicyTakeAway_Web.pd

Measuring and explaining efficiency of different countries responses to Covid-19 pandemic : a conditional robust nonparametric approach

In this paper, we propose the use of a conditional nonparametric robust estimator to evaluate countries responses to the outburst of COVID-19 pandemic. We collect data for 105 countries (comprehending the initial period of pandemic through the end of may/2021), with variables regarding the death toll, economic indicators, demographic characteristics and non-pharmaceutical interventions. We create a novel framework for estimating efficiency of countries responses in more general terms than simply evaluating healthcare system performance. We use two distinct well-known second-stage approaches: regressing the conditional efficiency scores on the environmental variables, in order to compute measures of managerial efficiency to rank responses; and regressing the ratio of conditional and unconditional scores on conditioning factors, seeking to explore the relationship between non-pharmaceutical interventions and the estimated efficiencies. Our results indicate which countries and regions stood out for presenting efficient/inefficient responses and point to a negative relationship between the variables median age, average stringency index and average retail and recreation visitors change and efficiency estimates.O objetivo deste trabalho é propor a aplicação de um estimador não paramétrico para avaliar a eficiência das respostas dos países à eclosão da pandemia de COVID-19. São coletados dados de 105 países (compreendendo o período inicial da pandemia até o final de maio de 2021), com variáveis que englobam o número de mortos, indicadores econômicos, características demográficas e intervenções não farmacológicas. Ao longo do texto são apresentadas as premissas utilizadas, que constituem um arcabouço inovador para estimar eficiência das respostas em termos mais gerais do que a simples avaliação da atuação do sistema de saúde. São implementadas duas técnicas de estimação em dois estágios, amplamente utilizadas na literatura: regressão dos scores de eficiência estimados contra as variáveis ambientais, com o objetivo de mensurar a eficiência gerencial e ranquear as respostas dos países; e uma regressão das razões dos scores condicionais e não condicionais contra os fatores condicionantes, buscando explorar a relação entre as medidas não farmacológicas e as estimativas de eficiência. Os resultados indicam quais países e regiões se destacaram, apresentando respostas mais eficientes/ineficientes, bem como apontam para uma relação negativo que as variáveis idade mediana, índice médio de restrição e alteração média no número de visitantes em lojas e locais recreativos tiveram nas estimativas de eficiência

The economic reaction to non-pharmaceutical interventions during Covid-19

Policy makers have implemented a set of non-pharmaceutical interventions (NPIs) to contain the spread of Covid-19 and reduce the burden on health systems. These restrictive measures have had adverse effects on economic activity; however, these negative impacts differ with respect to each country. Based on daily data, this article studies governmental economic responses to the application of NPIs for 59 countries. Furthermore, we assess if these economic responses differ according to the economic and sectoral context of the countries. By applying a counting model to the economic support intensity, our results quantify the average reaction of governments in counterbalancing the imposition of NPIs. We further re-estimate the base model by dividing the countries according to their GDP per capita, the intensity of their service sectors, and the expenditure by tourists. Our results show how each NPI implied a different level of economic support and how the structural characteristics considered were relevant to the decision-making process

Data integration and simulation of population immunity at the beginning of a pandemic

Accurate knowledge of population exposure at the outset of a pandemic has critical ramifications for preparedness plans for future epidemic waves. In this thesis, I developed a mechanistically informed statistical model to integrate multiple epidemiological datasets in different settings and in different population and to estimate key epidemiological parameters as well as population exposure using Bayesian inference. First, I present a dynamic model to link together three key metrics for evaluating the progress of COVID-19 epidemic in England: seroprevalence, PR-PCR test positivity and death. While estimating the IgG antibody seroreversion rate and region-specific infection fatality ratios, I find that epidemic progression resulted in an increasing gap between measured serology prevalence levels and cumulative population exposure to the virus. Ultimately, this may mean that twice as many, or more, people have been exposed to the virus relative to the number of people who are seropositive by the end of 2020. Moreover, I demonstrate that the model could reconstruct the first, unobserved, epidemic wave of COVID-19 in England from March 2020 to June 2020 as long as two or three serological measurements are given as inputs, with the second wave during the winter of 2020 validated by the estimates from the ONS Coronavirus Infection Survey. Comparing with the inferred exposure, I find that the UK official COVID-9 online dashboard reported COVID-19 cases only accounted for less than ten percent by the end of October 2020. I then generalise the model to account for the undocumented COVID-19-related mortality and sparse measurements of seroprevalence. I apply this in the context of Afghanistan COVID- 19 epidemic and find the population exposure in nine regions of Afghanistan were all higher than the seroprevalence survey suggested by July 2020. Finally, I assess the impact of shielding among pregnant patients by comparing their exposure with the estimated exposure of the general population. To approach this, I develop a dynamic model to link RT-PCR and antibody testing results from patients who gave birth and then apply Bayesian inference to estimate transmission parameters and exposure among pregnant patients. I find that after considering the duration of each pregnancy pre-COVID onset and after, the impact of shielding on reducing the level of exposure among pregnant patients during early 2020 who gave birth in this New York City hospital were approximately 50%

The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy

This paper estimates the effects of non-pharmaceutical interventions - mainly, the lockdown - on the COVID-19 mortality rate for the case of Italy, the first Western country to impose a national shelter-in-place order. We use a new estimator, the Augmented Synthetic Control Method (ASCM), that overcomes some limits of the standard Synthetic Control Method (SCM). The results are twofold. From a methodological point of view, the ASCM outperforms the SCM in that the latter cannot select a valid donor set, assigning all the weights to only one country (Spain) while placing zero weights to all the remaining. From an empirical point of view, we find strong evidence of the effectiveness of non-pharmaceutical interventions in avoiding losses of human lives in Italy: conservative estimates indicate that for each human life actually lost, in the absence of lockdown there would have been on average other 1.15, the policy saved in total 20,400 human lives.Comment: 34 pages, 6 figures, 3 table

Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries

Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number – a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of lockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented all interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-2 up to 28th March, representing between 1.88% and 11.43% of the population. The proportion of the population infected to date – the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-2 is slowing

Optimising time-limited non-pharmaceutical interventions for COVID-19 outbreak control

Retrospective analyses of the non-pharmaceutical interventions (NPIs) used to combat the ongoing COVID-19 outbreak have highlighted the potential of optimizing interventions. These optimal interventions allow policymakers to manage NPIs to minimize the epidemiological and human health impacts of both COVID-19 and the intervention itself. Here, we use a susceptible-infectious-recovered (SIR) mathematical model to explore the feasibility of optimizing the duration, magnitude and trigger point of five different NPI scenarios to minimize the peak prevalence or the attack rate of a simulated UK COVID-19 outbreak. An optimal parameter space to minimize the peak prevalence or the attack rate was identified for each intervention scenario, with each scenario differing with regard to how reductions to transmission were modelled. However, we show that these optimal interventions are fragile, sensitive to epidemiological uncertainty and prone to implementation error. We highlight the use of robust, but suboptimal interventions as an alternative, with these interventions capable of mitigating the peak prevalence or the attack rate over a broader, more achievable parameter space, but being less efficacious than theoretically optimal interventions. This work provides an illustrative example of the concept of intervention optimization across a range of different NPI strategies. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'