Towards an Accurate Estimation of COVID-19 Cases in Kazakhstan: Back-Casting and Capture–Recapture Approaches

Artificial Neural Network Prediction of COVID-19 Daily Infection Count

This study addresses COVID-19 testing as a nonlinear sampling problem, aiming to uncover the dependence of the true infection count in the population on COVID-19 testing metrics such as testing volume and positivity rates. Employing an artificial neural network, we explore the relationship among daily confirmed case counts, testing data, population statistics, and the actual daily case count. The trained artificial neural network undergoes testing in in-sample, out-of-sample, and several hypothetical scenarios. A substantial focus of this paper lies in the estimation of the daily true case count, which serves as the output set of our training process. To achieve this, we implement a regularized backcasting technique that utilize death counts and the infection fatality ratio (IFR), as the death statistics and serological surveys (providing the IFR) as more reliable COVID-19 data sources. Addressing the impact of factors such as age distribution, vaccination, and emerging variants on the IFR time series is a pivotal aspect of our analysis. We expect our study to enhance our understanding of the genuine implications of the COVID-19 pandemic, subsequently benefiting mitigation strategies.

Estimating actual SARS-CoV-2 infections from secondary data

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

Gaining further insights into the COVID-19 pandemic in Australia: Evidence using capture-recapture methods

Objective

We re-examined the reported number of COVID-19 cases in Australia and across its states during the first wave of the pandemic. We provided estimates of the total number of cases, adjusted for under-reporting.

Methods

Publicly available data sourced from Australian governments at federal, state and territory levels included records on cumulative confirmed COVID-19 cases and cumulative deaths occurring in Australia and across its states on a daily basis. Lower bound and upper bound estimates of the total number of COVID-19 cases in Australia and across its states, that included the undetected cases that have not been recorded, were estimated.

Results

On January 25, 2020, Australia recorded its first 4 cases of COVID-19 and the first death occurred on March 3, 2020. On April 1, 2020, 4864 cases had been reported with 21 deaths. Our estimation showed that on April 1, 2020, the minimum and maximum number of COVID-19 cases in Australia were in fact 10,160 (95 % CI: 9781-10,538) and 21,748 (95 % CI: 21,607-22,014) respectively. We estimated that the total number of cases were at least twice and at most four times the observed cases recorded. These differences were also found at the state level where in New South Wales there was a minimum and maximum of 207 and 447 cases in total for every 100 reported cases, while in Victoria these figures were much lower at 157 and 265 respectively for every 100 reported cases.

Conclusion

Case ascertainment during the pandemic is known to have been underestimated due to difficulties in testing and contact tracing, amongst others. Capture-recapture methods provided a measure of the gap between the official number of cases recorded and the actual number during the first wave of the pandemic.

Association of CYP24A1 Gene rs6127099 (A > T) Polymorphism with Lower Risk to COVID-19 Infection in Kazakhstan

In December 2019, SARS-CoV-2 was identified in Wuhan, China. Infection by SARS-CoV-2 causes coronavirus disease 2019 (COVID-19), which is characterized by fever, cough, dyspnea, anosmia, and myalgia in many cases. There are discussions about the association of vitamin D levels with COVID-19 severity. However, views are conflicting. The aim of the study was to examine associations of vitamin D metabolism pathway gene polymorphisms with symptomless COVID-19 susceptibility in Kazakhstan. The case-control study examined the association between asymptomatic COVID-19 and vitamin D metabolism pathway gene polymorphisms in 185 participants, who previously reported not having COVID-19, were PCR negative at the moment of data collection, and were not vaccinated. A dominant mutation in rs6127099 (CYP24A1) was found to be protective of asymptomatic COVID-19. Additionally, the G allele of rs731236 TaqI (VDR), dominant mutation in rs10877012 (CYP27B1), recessive rs1544410 BsmI (VDR), and rs7041 (GC) are worth consideration since they were statistically significant in bivariate analysis, although their independent effect was not found in the adjusted multivariate logistic regression model.