Spatiotemporal reproduction number with Bayesian model selection for evaluation of emerging infectious disease transmissibility: an application to COVID-19 national surveillance data.

BACKGROUND: To control emerging diseases, governments often have to make decisions based on limited evidence. The effective or temporal reproductive number is used to estimate the expected number of new cases caused by an infectious person in a partially susceptible population. While the temporal dynamic is captured in the temporal reproduction number, the dominant approach is currently based on modeling that implicitly treats people within a population as geographically well mixed. METHODS: In this study we aimed to develop a generic and robust methodology for estimating spatiotemporal dynamic measures that can be instantaneously computed for each location and time within a Bayesian model selection and averaging framework. A simulation study was conducted to demonstrate robustness of the method. A case study was provided of a real-world application to COVID-19 national surveillance data in Thailand. RESULTS: Overall, the proposed method allowed for estimation of different scenarios of reproduction numbers in the simulation study. The model selection chose the true serial interval when included in our study whereas model averaging yielded the weighted outcome which could be less accurate than model selection. In the case study of COVID-19 in Thailand, the best model based on model selection and averaging criteria had a similar trend to real data and was consistent with previously published findings in the country. CONCLUSIONS: The method yielded robust estimation in several simulated scenarios of force of transmission with computing flexibility and practical benefits. Thus, this development can be suitable and practically useful for surveillance applications especially for newly emerging diseases. As new outbreak waves continue to develop and the risk changes on both local and global scales, our work can facilitate policymaking for timely disease control.

Bayesian Spatio-Temporal Prediction and Counterfactual Generation: An Application in Non-Pharmaceutical Interventions in COVID-19.

The spatio-temporal course of an epidemic (such as COVID-19) can be significantly affected by non-pharmaceutical interventions (NPIs) such as full or partial lockdowns. Bayesian Susceptible-Infected-Removed (SIR) models can be applied to the spatio-temporal spread of infectious diseases (STIFs) (such as COVID-19). In causal inference, it is classically of interest to investigate the counterfactuals. In the context of STIF, it is possible to use nowcasting to assess the possible counterfactual realization of disease in an incidence that would have been evidenced with no NPI. Classic lagged dependency spatio-temporal IF models are discussed, and the importance of the ST component in nowcasting is assessed. Real examples of lockdowns for COVID-19 in two US states during 2020 and 2021 are provided. The degeneracy in prediction over longer time periods is highlighted, and the wide confidence intervals characterize the forecasts. For SC, the early and short lockdown contrasted with the longer NJ intervention. The approach here demonstrated marked differences in spatio-temporal disparities across counties with respect to an adherence to counterfactual predictions.

Bayesian space-time SIR modeling of Covid-19 in two US states during the 2020-2021 pandemic.

This paper describes the Bayesian SIR modeling of the 3 waves of Covid-19 in two contrasting US states during 2020-2021. A variety of models are evaluated at the county level for goodness-of-fit and an assessment of confounding predictors is also made. It is found that models with three deprivation predictors and neighborhood effects are important. In addition, the work index from Google mobility was also found to provide an increased explanation of the transmission dynamics.

Space-time covid-19 Bayesian SIR modeling in South Carolina.

The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided. The results suggest that both socio-economic disadvantage, asymptomatic transmission and spatial confounding are important ingredients in any model pertaining to county level case dynamics.

Patterns of within-host genetic diversity in SARS-CoV-2.

Monitoring the spread of SARS-CoV-2 and reconstructing transmission chains has become a major public health focus for many governments around the world. The modest mutation rate and rapid transmission of SARS-CoV-2 prevents the reconstruction of transmission chains from consensus genome sequences, but within-host genetic diversity could theoretically help identify close contacts. Here we describe the patterns of within-host diversity in 1181 SARS-CoV-2 samples sequenced to high depth in duplicate. 95.1% of samples show within-host mutations at detectable allele frequencies. Analyses of the mutational spectra revealed strong strand asymmetries suggestive of damage or RNA editing of the plus strand, rather than replication errors, dominating the accumulation of mutations during the SARS-CoV-2 pandemic. Within- and between-host diversity show strong purifying selection, particularly against nonsense mutations. Recurrent within-host mutations, many of which coincide with known phylogenetic homoplasies, display a spectrum and patterns of purifying selection more suggestive of mutational hotspots than recombination or convergent evolution. While allele frequencies suggest that most samples result from infection by a single lineage, we identify multiple putative examples of co-infection. Integrating these results into an epidemiological inference framework, we find that while sharing of within-host variants between samples could help the reconstruction of transmission chains, mutational hotspots and rare cases of superinfection can confound these analyses.

The COVID-19 pandemic has had major health impacts across the globe. The scientific community has focused much attention on finding ways to monitor how the virus responsible for the pandemic, SARS-CoV-2, spreads. One option is to perform genetic tests, known as sequencing, on SARS-CoV-2 samples to determine the genetic code of the virus and to find any differences or mutations in the genes between the viral samples. Viruses mutate within their hosts and can develop into variants that are able to more easily transmit between hosts. Genetic sequencing can reveal how genetically similar two SARS-CoV-2 samples are. But tracking how SARS-CoV-2 moves from one person to the next through sequencing can be tricky. Even a sample of SARS-CoV-2 viruses from the same individual can display differences in their genetic material or within-host variants. Could genetic testing of within-host variants shed light on factors driving SARS-CoV-2 to evolve in humans? To get to the bottom of this, Tonkin-Hill, Martincorena et al. probed the genetics of SARS-CoV-2 within-host variants using 1,181 samples. The analyses revealed that 95.1% of samples contained within-host variants. A number of variants occurred frequently in many samples, which were consistent with mutational hotspots in the SARS-CoV-2 genome. In addition, within-host variants displayed mutation patterns that were similar to patterns found between infected individuals. The shared within-host variants between samples can help to reconstruct transmission chains. However, the observed mutational hotspots and the detection of multiple strains within an individual can make this challenging. These findings could be used to help predict how SARS-CoV-2 evolves in response to interventions such as vaccines. They also suggest that caution is needed when using information on within-host variants to determine transmission between individuals.

Issues in Bayesian prospective surveillance of spatial health data.

In this paper I review some of the major issues that arise when geo-referenced health data are to be the subject of prospective surveillance. The review focusses on modelbased approaches to this activity, and proposes the Bayesian paradigm as a convenient vehicle for modeling. Various posterior functional measures are discussed including the SCPO and SKL and a number of extensions to these are considered. Overall the value of Bayesian Hierarchical Modeling (BHM) with surveillance functionals is stressed in its relevance to early warning of adverse risk scenarios.

The Effects of the COVID-19 Pandemic on Trauma Presentations in a Level One Trauma Center.

BACKGROUND: Over 28 million confirmed cases of COVID-19 have been reported to date, resulting in over 900 000 deaths. With an increase in awareness regarding the virus, the behavior of general population has changed dramatically. As activities such as driving and hospital presentation patterns have changed, our study aimed to assess the differences in trauma case variables before and during the COVID-19 pandemic. METHODS: Trauma data for the period of March 1st-June 15th were compared for the years 2015-2019 (pre-COVID) and 2020 (COVID). The data were analyzed across the following categories: injury severity score, injury mechanism, motor vehicle crashes (MVCs) vs. other blunt injuries, alcohol involvement, and length of hospital stay. RESULTS: The median injury severity score pre-COVID and during COVID was 9, representing no change. There was no difference in overall distribution of mechanism of injury; however, there was a significant decrease in the percentage of MVCs pre-COVID (36.39%) vs. COVID (29.6%, P < .05). Alcohol was significantly more likely to be involved in trauma during COVID-19 (P < .05). The mean hospital stay increased from 3.87-5.4 days during COVID-19 (P < .05). DISCUSSION: We saw similar results to prior studies in terms of there being no change in trauma severity. Our observation that motor vehicle collisions have decreased is consistent with current data showing decreased use of motor vehicles during the pandemic. We also observed an increase in alcohol-related cases which are consistent with the reported changes in alcohol consumption since the pandemic began.