Dataset Information

HIT-COVID, a global database tracking public health interventions to COVID-19.

ABSTRACT: The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making in response to the current and future pandemics. In order to estimate the effectiveness of these interventions, detailed descriptions of their timelines, scale and scope are needed. The Health Intervention Tracking for COVID-19 (HIT-COVID) is a curated and standardized global database that catalogues the implementation and relaxation of COVID-19 related PHSM. With a team of over 200 volunteer contributors, we assembled policy timelines for a range of key PHSM aimed at reducing COVID-19 risk for the national and first administrative levels (e.g. provinces and states) globally, including details such as the degree of implementation and targeted populations. We continue to maintain and adapt this database to the changing COVID-19 landscape so it can serve as a resource for researchers and policymakers alike.

SUBMITTER: Zheng Q

PROVIDER: S-EPMC7453020 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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HIT-COVID, a global database tracking public health interventions to COVID-19.

Zheng Qulu Q Jones Forrest K FK Leavitt Sarah V SV Ung Lawson L Labrique Alain B AB Peters David H DH Lee Elizabeth C EC Azman Andrew S AS

Scientific data 20200827 1

The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making in response to the current and future pandemics. In order to estimate the effectiveness of these interventions, detailed descriptions of their timelines, scale a ...[more]

PMID: 32855428

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Project description:Until less than two decades ago, all known human coronaviruses (CoV) caused diseases so mild that they did not stimulate further advanced CoV research. In 2002 and following years, the scenario changed dramatically with the advent of the new more pathogenic CoVs, including Severe Acute Respiratory Syndome (SARS-CoV-1), Middle Eastern respiratory syndrome (MERS)-CoV, and the new zoonotic SARS-CoV-2, likely originated from bat species and responsible for the present coronavirus disease (COVID-19), which to date has caused 15,581,007 confirmed cases and 635,173 deaths in 208 countries, including Italy. SARS-CoV-2 transmission is mainly airborne via droplets generated by symptomatic patients, and possibly asymptomatic individuals during incubation of the disease, although for the latter, there are no certain data yet. However, research on asymptomatic viral infection is currently ongoing worldwide to elucidate the real prevalence and mortality of the disease. From a clinical point of view, COVID-19 would be defined as "COVID Planet " because it presents as a multifaceted disease, due to the large number of organs and tissues infected by the virus. Overall, based on the available published data, 80.9% of patients infected by SARS-CoV-2 develop a mild disease/infection, 13.8% severe pneumonia, 4.7% respiratory failure, septic shock, or multi-organ failure, and 3% of these cases are fatal, but mortality parameter is highly variable in different countries. Clinically, SARS-CoV-2 causes severe primary interstitial viral pneumonia and a "cytokine storm syndrome", characterized by a severe and fatal uncontrolled systemic inflammatory response triggered by the activation of interleukin 6 (IL-6) with development of endothelitis and generalized thrombosis that can lead to organ failure and death. Risk factors include advanced age and comorbidities including hypertension, diabetes, and cardiovascular disease. Virus entry occurs via binding the angiotensin-converting enzyme 2 (ACE2) receptor present in almost all tissues and organs through the Spike (S) protein. Currently, SARS-CoV-2 infection is prevented by the use of masks, social distancing, and improved hand hygiene measures. This review summarizes the current knowledge on the main biological and clinical features of the SARS-CoV-2 pandemic, also focusing on the principal measures taken in some Italian regions to face the emergency and on the most important treatments used to manage the COVID-19 pandemic.

Project description:BackgroundUnprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic to control the spread of SARS-CoV-2 virus. It is a challenge to implement timely and appropriate public health interventions.Methods and findingsPopulation and COVID-19 epidemiological data between 21st January 2020 to 15th November 2020 from 216 countries and territories were included with the implemented public health interventions. We used deep reinforcement learning, and the algorithm was trained to enable agents to try to find optimal public health strategies that maximized total reward on controlling the spread of COVID-19. The results suggested by the algorithm were analyzed against the actual timing and intensity of lockdown and travel restrictions. Early implementations of the actual lockdown and travel restriction policies, usually at the time of local index case were associated with less burden of COVID-19. In contrast, our agent suggested to initiate at least minimal intensity of lockdown or travel restriction even before or on the day of the index case in each country and territory. In addition, the agent mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the policies implemented by governments, but did not always encourage rapid full lockdown and full border closures. The limitation of this study was that it was done with incomplete data due to the emerging COVID-19 epidemic, inconsistent testing and reporting. In addition, our research focuses only on population health benefits by controlling the spread of COVID-19 without balancing the negative impacts of economic and social consequences.InterpretationCompared to actual government implementation, our algorithm mostly recommended earlier intensity of lockdown and travel restrictions. Reinforcement learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.

Project description:BackgroundDifferent states in the United States had different nonpharmaceutical public health interventions during the COVID-19 pandemic. The effects of those interventions on hospital use have not been systematically evaluated. The investigation could provide data-driven evidence to potentially improve the implementation of public health interventions in the future.ObjectiveWe aim to study two representative areas in the United States and one area in China (New York State, Ohio State, and Hubei Province), and investigate the effects of their public health interventions by time periods according to key interventions.MethodsThis observational study evaluated the numbers of infected, hospitalized, and death cases in New York and Ohio from March 16 through September 14, 2020, and Hubei from January 26 to March 31, 2020. We developed novel Bayesian generalized compartmental models. The clinical stages of COVID-19 were stratified in the models, and the effects of public health interventions were modeled through piecewise exponential functions. Time-dependent transmission rates and effective reproduction numbers were estimated. The associations of interventions and the numbers of required hospital and intensive care unit beds were studied.ResultsThe interventions of social distancing, home confinement, and wearing masks significantly decreased (in a Bayesian sense) the case incidence and reduced the demand for beds in all areas. Ohio's transmission rates declined before the state's "stay at home" order, which provided evidence that early intervention is important. Wearing masks was significantly associated with reducing the transmission rates after reopening, when comparing New York and Ohio. The centralized quarantine intervention in Hubei played a significant role in further preventing and controlling the disease in that area. The estimated rates that cured patients become susceptible in all areas were small (<0.0001), which indicates that they have little chance to get the infection again.ConclusionsThe series of public health interventions in three areas were temporally associated with the burden of COVID-19-attributed hospital use. Social distancing and the use of face masks should continue to prevent the next peak of the pandemic.

Project description:BackgroundIn dealing with community spread of COVID-19, two active interventions have been attempted or advocated-containment, and mitigation. Given the extensive impact of COVID-19 globally, there is international interest to learn from best practices that have been shown to work in controlling community spread to inform future outbreaks. This study explores the trajectory of COVID-19 infection in Singapore had the government intervention not focused on containment, but rather on mitigation. In addition, we estimate the actual COVID-19 infection cases in Singapore, given that confirmed cases are publicly available.Methods and findingsWe developed a COVID-19 infection model, which is a modified SIR model that differentiate between detected (diagnosed) and undetected (undiagnosed) individuals and segments total population into seven health states: susceptible (S), infected asymptomatic undiagnosed (A), infected asymptomatic diagnosed (I), infected symptomatic undiagnosed (U), infected symptomatic diagnosed (E), recovered (R), and dead (D). To account for the infection stages of the asymptomatic and symptomatic infected individuals, the asymptomatic infected individuals were further disaggregated into three infection stages: (a) latent (b) infectious and (c) non-infectious; while the symptomatic infected were disaggregated into two stages: (a) infectious and (b) non-infectious. The simulation result shows that by the end of the current epidemic cycle without considering the possibility of a second wave, under the containment intervention implemented in Singapore, the confirmed number of Singaporeans infected with COVID-19 (diagnosed asymptomatic and symptomatic cases) is projected to be 52,053 (with 95% confidence range of 49,370-54,735) representing 0.87% (0.83%-0.92%) of the total population; while the actual number of Singaporeans infected with COVID-19 (diagnosed and undiagnosed asymptomatic and symptomatic infected cases) is projected to be 86,041 (81,097-90,986), which is 1.65 times the confirmed cases and represents 1.45% (1.36%-1.53%) of the total population. A peak in infected cases is projected to have occurred on around day 125 (27/05/2020) for the confirmed infected cases and around day 115 (17/05/2020) for the actual infected cases. The number of deaths is estimated to be 37 (34-39) among those infected with COVID-19 by the end of the epidemic cycle; consequently, the perceived case fatality rate is projected to be 0.07%, while the actual case fatality rate is estimated to be 0.043%. Importantly, our simulation model results suggest that there about 65% more COVID-19 infection cases in Singapore that have not been captured in the official reported numbers which could be uncovered via a serological study. Compared to the containment intervention, a mitigation intervention would have resulted in early peak infection, and increase both the cumulative confirmed and actual infection cases and deaths.ConclusionEarly public health measures in the context of targeted, aggressive containment including swift and effective contact tracing and quarantine, was likely responsible for suppressing the number of COVID-19 infections in Singapore.

Dataset Information

HIT-COVID, a global database tracking public health interventions to COVID-19.

Publications

HIT-COVID, a global database tracking public health interventions to COVID-19.

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