Panel forecasts of country-level Covid-19 infections.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu L
PROVIDER: S-EPMC7566698 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
Publications
Panel forecasts of country-level Covid-19 infections.
Liu Laura L Moon Hyungsik Roger HR Schorfheide Frank F
Journal of econometrics 20201016 1
We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our specification that assumes the growth rate of active infections can be represented by autoregressive fluctuations around a downward sloping deterministic trend function with a break. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of slopes and then implicitly use this distribution as prior to construct Bayes foreca ...[more]
Similar Datasets
Project description:BackgroundThere has been much debate about the effectiveness of lockdown measures in containing COVID-19, and their appropriateness given the economic and social cost they entail. To the best of our knowledge, no existing contribution to the literature has attempted to gauge the effectiveness of lockdown measures over time in a longitudinal cross-country perspective.ObjectivesThis paper aims to fill the gap in the literature by assessing, at an international level, the effect of lockdown measures (or the lack of such measures) on the numbers of new infections. Given this policy's expected change in effectiveness over time, we also measure the effect of having a lockdown implemented over a given number of days (from 7 to 20 days).MethodsWe pursue our objectives by means of a quantitative panel analysis, building a longitudinal dataset with observations from countries all over the world, and estimating the impact of lockdown via feasible generalized least squares fixed effect, random effects, generalized estimating equation, and hierarchical linear models.ResultsOur results show that lockdown is effective in reducing the number of new cases in the countries that implement it, compared with those countries that do not. This is especially true around 10 days after the implementation of the policy. Its efficacy continues to grow up to 20 days after implementation.ConclusionResults suggest that lockdown is effective in reducing the R0, i.e. the number of people infected by each infected person, and that, unlike what has been suggested in previous analyses, its efficacy continues to hold 20 days after the introduction of the policy.
| S-EPMC7268966 | biostudies-literature
Project description:Background: Not much is known about correlations between country-level characteristics and country-level numbers of COVID-19 cases and mortalities. Methods: Using data from the World Health Organization and other international organisations, we summarised country-level COVID-19 case and mortality counts per 100,000 population, and COVID-19 case fatality rate from January 2020 to August 2021. We conducted adjusted linear regression analysis to assess relationships between these counts/rate and certain country-level characteristics. We reported adjusted regression coefficients, β and associated 95% confidence intervals. Results: There was a positive correlation between the number of cases and country-level male/female ratio, and positive correlations between the numbers of cases and mortalities and country-level proportion of 60+-year-olds, universal health coverage index of service coverage (UHC) and tourism. Country economic status correlated negatively with the numbers of cases and mortalities. COVID-19 case fatality rate was highest in Peru, South American region (9.2%), and lowest in Singapore, Western Pacific region (0.1%). A negative correlation was observed between case fatality rate and country-level male/female ratio, population density and economic status. These observations remained mostly among mid-/low-income countries, particularly a positive correlation between the number of cases and male/female ratio and proportion of 60+-year-olds. Conclusions: Various country-level characteristics such as male/female ratio, proportion of older adults, country economic status, UHC and tourism appear to be correlated with the country-level number of COVID-19 cases and/or mortalities. Consideration of these characteristics may be necessary when designing country-level COVID-19 epidemiological studies and in comparing COVID-19 data between countries.
| S-EPMC9441611 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has overrun hospital systems while exacerbating economic hardship and food insecurity on a global scale. In an effort to understand how early action to find and control the virus is associated with cumulative outcomes, we explored how country-level testing capacity affects later COVID-19 mortality.MethodsWe used the Our World in Data database to explore testing and mortality records in 27 countries from December 31, 2019, to September 30, 2020; we applied Cox proportional hazards regression to determine the relationship between early COVID-19 testing capacity (cumulative tests per case) and later COVID-19 mortality (time to specified mortality thresholds), adjusting for country-level confounders, including median age, GDP, hospital bed capacity, population density, and nonpharmaceutical interventions.ResultsHigher early testing implementation, as indicated by more cumulative tests per case when mortality was still low, was associated with a lower risk for higher per capita deaths. A sample finding indicated that a higher cumulative number of tests administered per case at the time of six deaths per million persons was associated with a lower risk of reaching 15 deaths per million persons, after adjustment for all confounders (HR = 0.909; P = 0.0001).ConclusionsCountries that developed stronger COVID-19 testing capacity at early timepoints, as measured by tests administered per case identified, experienced a slower increase of deaths per capita. Thus, this study operationalizes the value of testing and provides empirical evidence that stronger testing capacity at early timepoints is associated with reduced mortality and improved pandemic control.
| S-EPMC8652724 | biostudies-literature
Project description:ObjectivesCharacterise the reopening policies of European countries after the first wave of infections and evaluate how these policies affected economic activity and subsequent infections.Study designUsing publicly available data, we construct a database of reopening policy announcements by country authorities and develop measures related to the speed and timing of reopening. Using panel data regressions, we then assess how a country's reopening action subsequently affected its mobility and COVID-19 infections. Samples of 22 European countries used in the study comprise: Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Ireland, Israel, Italy, Netherlands, Norway, Poland, Portugal, Romania, Russia, Spain, Switzerland, Turkey, Ukraine and the UK.Main outcomesMobility index as well as COVID-19 case and death counts.ResultsReopening policies are associated with a 1.5 percentage point increase in mobility and a 4% increase in subsequent infections after 2 weeks. However, some reopening strategies are associated with lower infection risk. In particular, early and fast reopeners saw 5%-10% increases in infections relative to those that opened later and adopted a gradual approach. The sequencing of sectoral reopenings matters, with infection amplification effects larger for some sectors (like retail and events) than others (like schools).ConclusionsFindings suggest some merit of gradual and late reopening strategies with a careful sequencing of sectoral openings based on their infection amplification risks.
| S-EPMC8829848 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has highlighted the role of infectious disease forecasting in informing public policy. However, significant barriers remain for effectively linking infectious disease forecasts to public health decision making, including a lack of model validation. Forecasting model performance and accuracy should be evaluated retrospectively to understand under which conditions models were reliable and could be improved in the future.MethodsUsing archived forecasts from the California Department of Public Health's California COVID Assessment Tool ( https://calcat.covid19.ca.gov/cacovidmodels/ ), we compared how well different forecasting models predicted COVID-19 hospitalization census across California counties and regions during periods of Alpha, Delta, and Omicron variant predominance.ResultsBased on mean absolute error estimates, forecasting models had variable performance across counties and through time. When accounting for model availability across counties and dates, some individual models performed consistently better than the ensemble model, but model rankings still differed across counties. Local transmission trends, variant prevalence, and county population size were informative predictors for determining which model performed best for a given county based on a random forest classification analysis. Overall, the ensemble model performed worse in less populous counties, in part because of fewer model contributors in these locations.ConclusionsEnsemble model predictions could be improved by incorporating geographic heterogeneity in model coverage and performance. Consistency in model reporting and improved model validation can strengthen the role of infectious disease forecasting in real-time public health decision making.
| S-EPMC10141909 | biostudies-literature
Project description:We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention for the epidemic in the United States. We find three main results. First, at the short horizon (1-week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3- and 4-week ahead) forecasters are more successful and sometimes outperform the benchmark. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior approach for health authorities, rather than relying on a small number of forecasts.
| S-EPMC8801780 | biostudies-literature
Project description:As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks.
| S-EPMC9188562 | biostudies-literature
Project description:BackgroundThe association between social capital and preventive behaviors against COVID-19 remains controversial. We examined the association between social capital and preventive behaviors against COVID-19 at country level.MethodsThe data on country-level social capital (i.e., social trust, group affiliations, civic responsibility, and confidence in state institutions) was obtained from a previous literature based on World Value Survey. Preventive behaviors were calculated as the percentage of people in a country who took COVID-19 preventive behaviors (i.e., physical distancing, hand hygiene, and the use of face mask) from an international survey of Facebook users, from July to October 2020 (207 data points for 9 waves of 23 countries). The scores on social capital were standardized. The association was investigated with multilevel linear regression analysis.ResultsHigh civic responsibility (per 1 standard deviation, SD) was associated with low percentage points of physical distancing (β = -4.66, 95% confidence interval, CI: 7.23, -2.09), hand hygiene (β = -2.88, 95% CI: 3.98, -1.78) and the use of face mask (β = -3.95, 95% CI: 5.29, -2.62). Group affiliations were associated with high percentage points of physical distancing (β = 2.96, 95% CI: 0.35, 5.58) and the use of face mask (β = 1.80, 95% CI: 0.45, 3.16). Social trust had significant positive association with performing hand hygiene (β = 1.22, 95% CI: 0.09, 2.35).ConclusionsThese results suggested that in countries with higher levels of civic responsibility, preventive behaviors should be more intensified during a pandemic.
| S-EPMC9523911 | biostudies-literature
Project description:The Coronavirus Disease 2019 (COVID-19) pandemic has had a variable worldwide impact, likely related to country-level characteristics. In this ecological study, we explored the association of COVID-19 case rates (per 100,000 people) and death rates (per 100,000 people) with country-level population health characteristics, economic and human development indicators, and habitat-related variables. To calculate country-level COVID-19 case and death rates, the number of cases and deaths were extracted from the Johns Hopkins Coronavirus Resource Center through September 30, 2021. Country-level population health characteristics, economic, human development, and habitat-related indicators were extracted from several publicly available online sources of international organizations. Results were tabulated according to world zones and country economies. Unadjusted and adjusted multiple imputation linear regression analyses were performed to examine the association between country-level variables (per 1-standard deviation [SD] increase) and COVID-19 case and death rates. To satisfy the linear regression model assumptions of normality of residuals, we used the square root transformation of both outcomes. A total of 187 countries and territories were analyzed, with a median (25th, 75th percentiles) aggregate COVID-19 case rate of 3,605 (463, 8,228) per 100,000, a COVID-19 death rate of 45.9 (8.9, 137.1) per 100,000, and a case-fatality rate of 1.6% (1.2%, 2.6%). On multivariable analyses, each country-level 1-SD higher percentage of adults with obesity (β coefficient 13.7; 95% confidence interval [CI] 13.7; 8.9, 18.4), percentage of smokers (5.8; 95% CI 1.2, 10.5), percentage of adults with high blood pressure (4.9; 95% CI 0.3, 9.6), and gross national income (GNI) per capita (9.5; 95% CI 4.6, 14.5) was independently associated with higher square root of COVID-19 case rate, while average household size (-1.7; 95% CI -12.3, -3.2) was independently associated with lower square root of COVID-19 case rate. Similarly, each 1-SD higher percentage of adults with obesity (1.76; 95% CI 0.99, 2.52), percentage of adults with high blood pressure (1.11; 95% CI 0.48, 1.74), percentage of adults with physical inactivity (1.01; 95% CI 0.10, 1.191), and travel & tourism competitiveness index (1.05; 95% CI 0.06, 2.04) was independently associated with higher square root of COVID-19 death rate, whereas GNI per capita (-0.92; 95% CI -1.81, -0.03), and average household size (-1.07; 95% CI -1.87, -0.27) was independently associated with lower square root of COVID-19 death rate. This ecological study informs the need to develop country-specific public health interventions to better target populations at high risk for COVID-19, and test interventions to prevent transmission of SARS-CoV-2, taking into consideration cross-country differences in population health characteristics, and economic, human development and habitat-related factors.
| S-EPMC8662119 | biostudies-literature
Project description:The identification of factors associated to COVID-19 mortality is important to design effective containment measures and safeguard at-risk categories. In the last year, several investigations have tried to ascertain key features to predict the COVID-19 mortality tolls in relation to country-specific dynamics and population structure. Most studies focused on the first wave of the COVID-19 pandemic observed in the first half of 2020. Numerous studies have reported significant associations between COVID-19 mortality and relevant variables, for instance obesity, healthcare system indicators such as hospital beds density, and bacillus Calmette-Guerin immunization. In this work, we investigated the role of ABO/Rh blood groups at three different stages of the pandemic while accounting for demographic, economic, and health system related confounding factors. Using a machine learning approach, we found that the "B+" blood group frequency is an important factor at all stages of the pandemic, confirming previous findings that blood groups are linked to COVID-19 severity and fatal outcome.
| S-EPMC8720090 | biostudies-literature