Inferring the number of COVID-19 cases from recently reported deaths.
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ABSTRACT:
SUBMITTER: Jombart T
PROVIDER: S-EPMC7255910 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Inferring the number of COVID-19 cases from recently reported deaths.
Jombart Thibaut T van Zandvoort Kevin K Russell Timothy W TW Jarvis Christopher I CI Gimma Amy A Abbott Sam S Clifford Sam S Funk Sebastian S Gibbs Hamish H Liu Yang Y Pearson Carl A B CAB Bosse Nikos I NI Eggo Rosalind M RM Kucharski Adam J AJ Edmunds W John WJ
Wellcome open research 20200427
We estimate the number of COVID-19 cases from newly reported deaths in a population without previous reports. Our results suggest that by the time a single death occurs, hundreds to thousands of cases are likely to be present in that population. This suggests containment via contact tracing will be challenging at this point, and other response strategies should be considered. Our approach is implemented in a publicly available, user-friendly, online tool. ...[more]
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Project description:We estimate the number of COVID-19 cases from newly reported deaths in a population without previous reports. Our results suggest that by the time a single death occurs, hundreds to thousands of cases are likely to be present in that population. This suggests containment via contact tracing will be challenging at this point, and other response strategies should be considered. Our approach is implemented in a publicly available, user-friendly, online tool.
| S-EPMC7239087 | biostudies-literature
Project description:BackgroundThe rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country.MethodsIn this paper we apply the previously validated approach of phenomenological models, fitting several non-linear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period.ResultsWe observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551-26,702 cases in 5 days and an additional 47,449-57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional 145-437 COVID-19 deaths in 5 days and an additional 243-947 deaths in 10 days.ConclusionsBy comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.
| S-EPMC7797353 | biostudies-literature
Project description:ImportanceFederal data underestimate the impact of COVID-19 on US nursing homes because federal reporting guidelines did not require facilities to report case and death data until the week ending May 24, 2020.ObjectiveTo assess the magnitude of unreported cases and deaths in the National Healthcare Safety Network (NHSN) and provide national estimates of cases and deaths adjusted for nonreporting.Design, setting, and participantsThis is a cross-sectional study comparing COVID-19 cases and deaths reported by US nursing homes to the NHSN with those reported to state departments of health in late May 2020. The sample includes nursing homes from 20 states, with 4598 facilities in 12 states that required facilities to report cases and 7401 facilities in 19 states that required facilities to report deaths. Estimates of nonreporting were extrapolated to infer the national (15 397 facilities) unreported cases and deaths in both May and December 2020. Data were analyzed from December 2020 to May 2021.ExposuresNursing home ownership (for-profit or not-for-profit), chain affiliation, size, Centers for Medicare & Medicaid Services star rating, and state.Main outcomes and measuresThe main outcome was the difference between the COVID-19 cases and deaths reported by each facility to their state department of health vs those reported to the NHSN.ResultsAmong 15 415 US nursing homes, including 4599 with state case data and 7405 with state death data, a mean (SE) of 43.7% (1.4%) of COVID-19 cases and 40.0% (1.1%) of COVID-19 deaths prior to May 24 were not reported in the first NHSN submission in sample states, suggesting that 68 613 cases and 16 623 deaths were omitted nationwide, representing 11.6% of COVID-19 cases and 14.0% of COVID-19 deaths among nursing home residents in 2020.Conclusions and relevanceThese findings suggest that federal NHSN data understated total cases and deaths in nursing homes. Failure to account for this issue may lead to misleading conclusions about the role of different facility characteristics and state or federal policies in explaining COVID outbreaks.
| S-EPMC8430452 | biostudies-literature
Project description:BackgroundIn Japan, vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was initiated on 17 February 2021, mainly using messenger RNA vaccines and prioritizing health care professionals. Whereas nationwide vaccination alleviated the coronavirus disease 2019 (COVID-19)-related burden, the population impact has yet to be quantified in Japan. We aimed to estimate the numbers of COVID-19 cases and deaths prevented that were attributable to the reduced risk among vaccinated individuals via a statistical modeling framework.MethodsWe analyzed confirmed cases registered in the Health Center Real-time Information-sharing System on COVID-19 (3 March-30 November 2021) and publicly reported COVID-19-related deaths (24 March-30 November 2021). The vaccination coverage over this time course, classified by age and sex, was extracted from vaccine registration systems. The total numbers of prevented cases and deaths were calculated by multiplying the daily risk differences between unvaccinated and vaccinated individuals by the population size of vaccinated individuals.FindingsFor both cases and deaths, the averted numbers were estimated to be the highest among individuals aged 65 years and older. In total, we estimated that 564,596 (95% confidence interval: 477,020-657,525) COVID-19 cases and 18,622 (95% confidence interval: 6522-33,762) deaths associated with SARS-CoV-2 infection were prevented owing to vaccination during the analysis period (i.e., fifth epidemic wave, caused mainly by the Delta variant). Female individuals were more likely to be protected from infection following vaccination than male individuals whereas more deaths were prevented in male than in female individuals.InterpretationThe vaccination program in Japan led to substantial reductions in the numbers of COVID-19 cases and deaths (33% and 67%, respectively). The preventive effect will be further amplified during future pandemic waves caused by variants with shared antigenicity.FundingThis project was supported by the Japan Science and Technology Agency; the Japan Agency for Medical Research and Development; the Japan Society for the Promotion of Science; and the Ministry of Health, Labour and Welfare.
| S-EPMC9366235 | biostudies-literature
Project description:As of July 2021, Montreal is the epicentre of the COVID-19 pandemic in Canada with highest number of deaths. We aim to investigate the spatial distribution of the number of cases and deaths due to COVID-19 across the boroughs of Montreal. To this end, we propose that the cumulative numbers of cases and deaths in the 33 boroughs of Montreal are modelled through a bivariate hierarchical Bayesian model using Poisson distributions. The Poisson means are decomposed in the log scale as the sums of fixed effects and latent effects. The areal median age, the educational level, and the number of beds in long-term care homes are included in the fixed effects. To explore the correlation between cases and deaths inside and across areas, three different bivariate models are considered for the latent effects, namely an independent one, a conditional autoregressive model, and one that allows for both spatially structured and unstructured sources of variability. As the inclusion of spatial effects change some of the fixed effects, we extend the Spatial+ approach to a Bayesian areal set up to investigate the presence of spatial confounding. We find that the model which includes independent latent effects across boroughs performs the best among the ones considered, there appears to be spatial confounding with the diploma and median age variables, and the correlation between the cases and deaths across and within boroughs is always negative.
| S-EPMC9126618 | biostudies-literature
Project description:The SARS-COV-2 virus, also known as the coronavirus, has spread around the world. A growing literature suggests that exposure to pollution can cause respiratory illness and increase deaths among the elderly. However, little is known about whether increases in pollution could cause additional or more severe infections from COVID-19, which typically manifests as a respiratory infection. During the pandemic, the Environmental Protection Agency (EPA) rolled back enforcement of environmental regulation, causing an increase in pollution in counties with more TRI sites. We use the variation in pollution and a difference in differences design to estimate the effects of increased pollution on county-level COVID-19 deaths and cases. We find that counties with more Toxic Release Inventory (TRI) sites saw a 11.8 percent increase in pollution on average following the EPA's rollback of enforcement, compared to counties with fewer TRI sites. We also find that these policy-induced increases in pollution are associated with a 53 percent increase in cases and a 10.6 percent increase in deaths from COVID-19.
| S-EPMC7899033 | biostudies-literature
Project description:The novel coronavirus SARS-CoV-2 uses the angiotensin-converting enzyme 2 receptor as an entry point to the cell. Cardiovascular disease (CVD) is a risk factor for COVID-19 with poor outcomes. We tested the hypothesis that the rate of angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) use is associated with the rate of COVID-19-confirmed cases and deaths. We conducted a geospatial, ecological study using publicly available county-level data. The Medicare ACEI and ARB prescription rate was exposure. The COVID-19-confirmed case and death rates were outcomes. Spatial autoregression models were adjusted for the rate of births and deaths; Group Quarters population; percentage of female; percentage of Native American, Pacific Islander, Hispanic, and Black; percentage of children and older (>65 years) adults; percentage of uninsured; percentage of those living in poverty; percentage of those who are obese, smoking, admitting insufficient sleep, and those with at least some college degree; median household income; air quality index; CVD hospitalization rate in Medicare beneficiaries; and CVD death rate in a total county population. After adjustment for confounders, the ACEI use rate did not associate with COVID-19-confirmed case rate (direct county-own effect + 0.027%; 95% confidence interval [CI] -1.080 to 1.134; p = 0.962; indirect spillover effect + 0.26%; 95% CI -70.0 to 70.5; p = 0.994). Similarly, the ARB use rate was not associated with COVID-19-confirmed case rate (direct effect + 0.029%; 95% CI -0.803 to 0.862; p = 0.945; indirect effect + 0.19%; 95% CI -52.8 to 53.2; p = 0.994). In both unadjusted and adjusted Bayesian zero inflation Poisson analysis, neither ACEI nor ARB use rates were associated with COVID-19 death rates. In conclusion, ACEI and ARB use rates were not associated with COVID-19 infectivity and death rate in this ecological study.
| S-EPMC8665655 | biostudies-literature
Project description:Background: The purpose of the study was to compare trends in the progression of COVID-19 among South Asian countries with more developed Western countries. Methods: COVID-19 data from South Asian countries were used for this observational study. Data were taken up to April 21, 2020 from the outbreak of the COVID-19. Four of the seven countries met the inclusion criteria and were included in the analysis. Results: An exponential increase in the average number of weekly cases was reported after the fifth week following the first case. The correlation between reported cases and tests was found to be strong and significant (r=0.90, p=0.037). However, on average, 315.25 tests per million population were performed, which was at least 12 times lower than the number of tests performed in countries with a large number of COVID-19 cases. Conclusions: At present, the number of confirmed cases from South Asia was found to be significantly lower than in Western countries. Hence, an increase in the strength of performing diagnostic tests is highly recommended. Strict measures are required to make the people of these countries follow the instructions of social distancing and comply with preventive measures.
| S-EPMC7905493 | biostudies-literature
Project description:As the COVID-19 pandemic has progressed, so too has the recognition that cases and deaths have been underreported, perhaps vastly so. Here, we present an econometric strategy to estimate the true number of COVID-19 cases and deaths for 61 and 56 countries, respectively, from 1 January 2020 to 3 November 2020. Specifically, we estimate a 'structural' model based on the SIR epidemiological model extended to incorporate underreporting. The results indicate significant underreporting by magnitudes that align with existing research and conjectures by public health experts. While our approach requires some strong assumptions, these assumptions are very different from the equally strong assumptions required by other approaches addressing underreporting in the assessment of the extent of the pandemic. Thus, we view our approach as a complement to existing methods.
| S-EPMC9115431 | biostudies-literature
Project description:BackgroundRecurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models.MethodsWe used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios.ResultsAt a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12-50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics.DiscussionOur model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.
| S-EPMC11361242 | biostudies-literature