Assessing the impact of human mobility to predict regional excess death in Ecuador

COVID-19 outbreaks have had high mortality in low- and middle-income countries such as Ecuador. Human mobility is an important factor influencing the spread of diseases possibly leading to a high burden of disease at the country level. Drastic control measures, such as complete lockdown, are effective epidemic controls, yet in practice one hopes that a partial shutdown would suffice. It is an open problem to determine how much mobility can be allowed while controlling an outbreak. In this paper, we use statistical models to relate human mobility to the excess death in Ecuador while controlling for demographic factors. The mobility index provided by GRANDATA, based on mobile phone users, represents the change of number of out-of-home events with respect to a benchmark date (March 2nd, 2020). The study confirms the global trend that more men are dying than expected compared to women, and that people under 30 show less deaths than expected, particularly individuals younger than 20 with a death rate reduction between 22 and 27%. The weekly median mobility time series shows a sharp decrease in human mobility immediately after a national lockdown was declared on March 17, 2020 and a progressive increase towards the pre-lockdown level within two months. Relating median mobility to excess deaths shows a lag in its effect: first, a decrease in mobility in the previous two to three weeks decreases excess death and, more novel, we found an increase of mobility variability four weeks prior increases the number of excess deaths.

Estimation of excess all-cause mortality due to COVID-19 in Thailand

Background: Thailand has recently experienced the most prominent COVID-19 outbreak, resulting in a new record for COVID-19 cases and deaths. To assess the influence of the COVID-19 outbreak on mortality, we aimed to estimate excess mortality in Thailand. Methods: We estimated the baseline number of deaths in the absence of COVID-19 using generalized linear mixed models (GLMMs). The models were adjusted for seasonality and demographics. We evaluated the excess mortality from April to October 2021 in Thailand. Results: We found that the estimated cumulative excess death from April to October 2021 was 14.3% (95% CI: 8.6%-18.8%) higher than the baseline. The results also showed that the excess deaths in males were higher than in females by approximately 26.3%. The excess deaths directly caused by the COVID-19 infections accounted for approximately 75.0% of the all-cause excess deaths. Furthermore, the cumulative COVID-19 cases were found to be correlated with the cumulative excess deaths with a correlation coefficient of 0.9912 (95% CI, 0.9392-0.9987). Conclusions: The recent COVID-19 outbreak in Thailand significantly impacts mortality and affects people for specific ages and sex. During the outbreak in 2021, there was a significant rise in excess fatalities, especially in the older age groups. The increase in mortality was higher in men than in women.

South African Population Immunity and Severe Covid-19 with Omicron Variant

Background We conducted a seroepidemiological survey from October 22 to December 9, 2021, in Gauteng Province, South Africa, to determine SARS-CoV-2 immunoglobulin G (IgG) seroprevalence primarily prior to the fourth wave of coronavirus disease 2019 (Covid-19), in which the B.1.1.529 (Omicron) variant is dominant. We evaluated epidemiological trends in case rates and rates of severe disease through to December 15, 2021, in Gauteng. Methods We contacted households from a previous seroepidemiological survey conducted from November 2020 to January 2021, plus an additional 10% of households using the same sampling framework. Dry blood spot samples were tested for anti-spike and anti-nucleocapsid protein IgG using quantitative assays on the Luminex platform. Daily case and death data, weekly excess deaths, and weekly hospital admissions were plotted over time. Results Samples were obtained from 7010 individuals, of whom 1319 (18.8%) had received a Covid-19 vaccine. Overall seroprevalence ranged from 56.2% (95% confidence interval [CI], 52.6 to 59.7) in children aged <12 years to 79.7% (95% CI, 77.6 to 81.5) in individuals aged >50 years. Seropositivity was 6.22-fold more likely in vaccinated (93.1%) vs unvaccinated (68.4%) individuals. Epidemiological data showed SARS-CoV-2 infection rates increased more rapidly than in previous waves but have now plateaued. Rates of hospitalizations and excess deaths did not increase proportionately, remaining relatively low. Conclusions We demonstrate widespread underlying SARS-CoV-2 seropositivity in Gauteng Province prior to the current Omicron-dominant wave, with epidemiological data showing an uncoupling of hospitalization and death rates from infection rate during Omicron circulation.

Estimation of R0 for the spread of SARS-CoV-2 in Germany from Excess Mortality

Background For SARS-CoV-2, R0 calculations in the range of 2-3 dominate the literature, but much higher estimates have also been published. Because capacity for PCR testing increased greatly in the early phase of the Covid-19 pandemic, R0 determinations based on these incidence values are subject to strong bias. We propose to use Covid-19-induced excess mortality to determine R0 regardless of PCR testing capacity. Methods We used data from the Robert Koch Institute (RKI) on the incidence of Covid cases, Covid-related deaths, number of PCR tests performed, and excess mortality calculated from data from the Federal Statistical Office in Germany. We determined R0 using exponential growth estimates with a serial interval of 4.7 days. We used only datasets that were not yet under the influence of policy measures (e.g., lockdowns or school closures). Results The uncorrected R0 value for the spread of SARS-CoV-2 based on PCR incidence data was 2.56 (95% CI 2.52-2.60) for Covid-19 cases and 2.03 (95%CI 1.96-2.10) for Covid-19-related deaths. However, because the number of PCR tests increased by a growth factor of 1.381 during the same period, these R0 values must be corrected accordingly (R0corrected = R0uncorrected/1.381), yielding 1.86 for Covid-19 cases and 1.47 for Covid-19 deaths. The R0 value based on excess deaths was calculated to be 1.34 (95% CI 1.32-1.37). A sine-function-based adjustment for seasonal effects of 40% corresponds to a maximum value of R0January = 1.68 and a minimum value of R0July = 1.01. Discussion Our calculations show an R0 that is much lower than previously thought. This relatively low range of R0 fits very well with the observed seasonal pattern of infection across Europe in 2020 and 2021, including the emergence of more contagious escape variants such as delta or omicron. In general, our study shows that excess mortality can be used as a reliable surrogate to determine the R0 in pandemic situations.

A Scaling Law for PCR Positivity in the COVID Second Wave

In this preliminary report, PCR positivity data in the second wave of the COVID pandemic (September-January 2020) are shown to obey a scaling law given by: where % P0 and Σ0 are the y- and x-intercepts of a plot of positivity, %P, against the number of tests, Σ. The law holds across international, regional and local boundaries, as demonstrated for Great Britain, Austria, Germany and Sweden, the nine English regions, London - Yorkshire & Humber, and various Local Health Authorities in England. One possible explanation for scaling might be Dorfman pooling.The scaling law can be used to remove a systematic or false positive (FP) component from the daily number of positive tests, or cases, to yield the real number of cases. The results correlate strongly with the ZOE survey for London (R2=0.787) and Excess Deaths for England (R2=0.833). The cumulative total of FPs can be estimated as 1.4M by the beginning of 2021, in line with other estimates.

A Disparity in COVID Numbers: Simulation of Trump v. Obama

Previous studies of excess deaths and infections have come out about the coronavirus during President Donald J. Trump’s administration. With President Trump’s inactions, funding cuts, and statements underestimating the impact of COVID, there have been criticisms about how President Trump handled the coronavirus throughout his presidential term. It brings about the question -- What would the case number of COVID deaths and infections be like if President Barack H. Obama, the prior president, was in office? This study uses an open source Python simulation in order to estimate the number of COVID deaths and infections estimated under Trump and Obama’s administration. By doing a case study between the two, these results provide information regarding the number of lives that could potentially have been saved along with potential insight into finding preventative measures to save lives.

SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation

Introduction: Fervourous investigation and dialogue surrounding the true number of SARS-CoV-2-related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nation’s devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from 1 April 2020 to 30 June 2021. Methods: Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv and SSRN for preprints (accessed through iSearch), were searched on 3 July 2021 (with results verified through 15 August 2021). Altogether, using a two-step approach, 4,765 initial citations were screened, resulting in 37 citations included in the narrative review and 19 studies with 41datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analysed IFR1, which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections, and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provided lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2-related IFRs in India. We also tried to stratify our empirical results across the first and second waves. In tandem, we presented updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from 1 April 2020 to 30 June 2021. Results: For India, countrywide, the underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3 to 29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4 to 11.9 with cumulative excess deaths ranging from 1.79 to 4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067–0.140) and 0.365% (95% CI: 0.264–0.504) to 0.485% (95% CI: 0.344–0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, IFR1 generally appears to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290–1.316) to (0.241–0.651)%). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097–0.116) and 0.367% (95% CI: 0.358–0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data, with the disadvantage being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1. Conclusion: When incorporating case and death underreporting, the meta-analysed cumulative infection fatality rate in India varied from 0.36 to 0.48%, with a case underreporting factor ranging from 25 to 30 and a death underreporting factor ranging from 4 to 12. This implies, by 30 June 2021, India may have seen nearly 900 million infections and 1.7–4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (Coronavirus in India) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India. JEL Classifications: I15, I18