Scaling law of COVID-19 cases and city size and its evolution over time

Urban scaling law quantifies the disproportional growth of urban indicators with urban population size, which is one of the simple rules behind the complex urban system. Infectious diseases are closely related to social interactions that intensify in large cities, resulting in a faster speed of transmission in large cities. However, how this scaling relationship varies in an evolving pandemic is rarely investigated and remains unclear. Here, taking the COVID- 19 epidemic in the United States as an example, we collected daily added cases and deaths from January 2020 to June 2022 in more than three thousand counties to explore the scaling law of COVID- 19 cases and city size and its evolution over time. Results show that COVID- 19 cases super- linearly scaled with population size, which means cases increased faster than population size from a small city to a large city, resulting in a higher morbidity rate of COVID- 19 in large cities. Temporally, the scaling exponent that reflects the scaling relationship stabilized at around 1.25 after a fast increase from less than one. The scaling exponent gradually decreased until it was close to one. In comparison, deaths caused by the epidemic did not show a super-linear scaling relationship with population size, which revealed that the fatality rate of COVID-19 in large cities was not higher than that in small or medium-sized cities. The scaling exponent of COVID- 19 deaths shared a similar trend with that of COVID- 19 cases but with a lag in time. We further estimated scaling exponents in each wave of the epidemic, respectively, which experienced the common evolution process of first rising, then stabilizing, and then decreasing. We also analyzed the evolution of scaling exponents over time from regional and provincial perspectives. The northeast, where New York State is located, had the highest scaling exponent, and the scaling exponent of COVID- 19 deaths was higher than that of COVID-19 cases, which indicates that large cities in this region were more prominently affected by the epidemic. This study reveals the size effect of infectious diseases based on the urban scaling law, and the evolution process of scaling exponents over time also promotes the understanding of the urban scaling law. The mechanism behind temporal variations of scaling exponents is worthy of further exploration. © 2023 Science Press. All rights reserved.

The impact of deprivation on Respiratory Support Unit outcomes in COVID-19 patients, and highlights from wave 2 data in Portsmouth

Background: The second wave of the COVID-19 pandemic caused significant demand for beds capable of delivering enhanced respiratory support. NHS England recommended the use of CPAP for patients with COVID-19 respiratory failure, a treatment which can be offered outside of a critical care facility, and on a Respiratory High Care/ Support Unit (RSU). The enhancement of Portsmouth's RSU provided CPAP and NIV for patients with COVID-19 respiratory failure. With our intensive care facilities at 300% their normal capacity, this greatly alleviated bed pressures on critical care. Varied levels of deprivation exist in Portsmouth's dense population. Deprivation has an impact on overall health, however the effect of postcode on outcomes for people going onto support for COVID-19 respiratory failure, is unknown. Method(s): Retrospective cohort analysis of consecutive patients admitted to Respiratory Support Unit during the second wave of the COVID-19 pandemic, from 02/11/2020 to 31/01/2021. 227 patients were included in the study with 8 removed due to incomplete data, all of the patients received respiratory support in the form of CPAP or NIV. We collected multivariate data including biochemical markers, demographics, oxygenation status, co-morbidities and outcomes. Outcomes measured were: 1) Death in RSU, 2) Discharge from RSU or 3) Intubation and Ventilation. To measure deprivation, we linked a persons postcode to an area called an LSOA (Lower-layer Super Output Area). These are small areas of similar population size, each of which has a deprivation score (ie. top 10%, to the lowest 10% areas of deprivation in the UK). This is measured using an 'index of multiple deprivation'. An individual's outcome from the RSU was then analysed in relation to the deprivation score allocated to their postcode. Result(s): We observed a significant number of patients discharged from RSU, without needing invasive mechanical ventilation. 80/219 were discharged directly. 45/219 died in RSU, and 94 were eventually admitted to ITU. The average stay on CPAP or NIV before needing admission to ITU was 3 days. Some biochemical markers which stood out in relation to the outcomes described were as follows: average LDH, D-dimer and Troponin levels were higher in those who were admitted to intensive care. In patients who died, the PCT was significantly higher on average when compared to the other two groups. In the group who were discharged, mean lymphocyte count was >1, in the other two groups this was <1. From our observations in Portsmouth, there is a negative correlation between deprivation and lower aged individuals admitted for COVID-19 related respiratory support. Overall, we also saw disproportionate representation of those from the most deprived 50% of the UK in our respiratory support unit. Conclusion(s): CPAP and NIV can effectively be used in an RSU during a spike of COVID-19, to safely minimise demand on critical care services. Deprivation may have an impact on outcomes in patients needing respiratory support related to COVID-19. Deprivation levels may help predict risk of needing enhanced respiratory support in certain age groups. Multiple biochemical markers may be of prognostic value in COVID-19.

Association of state-level COVID-19 mortality rates with real-world progression and real-world overall survival among patients with advanced non-small cell lung cancer, 2020-2022

Background: The COVID-19 pandemic impacted the delivery of cancer care and outcomes in the United States (US). We examined the association between time-varying state-level weekly COVID19 mortality and progression-free survival (rwPFS), time to progression (rwTTP), and survival (rwOS) among pts with advanced non-small cell lung cancer (advNSCLC). Method(s): This retrospective study used the nationwide Flatiron Health electronic health recordderived de-identified database. The cohort included community oncology pts diagnosed with advNSCLC between March 1, 2020 and December 31, 2021 (follow-up through March 30, 2022). We extracted US data on COVID-19 deaths from the COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University. We calculated state-level weekly COVID-19 death rates as weekly COVID-19 deaths per state population size from the 2019 American Community Survey. We categorized rates into quintiles based on all weekly rates during the observation period. Analyses were restricted to treated pts and indexed to start of first-line therapy. For rwPFS analyses, first occurrence of progression or death was considered an event, and pts were censored at last clinic note date. For rwTTP, only progression (not death) was considered an event, and pts with no event were censored at last clinic note date. For rwOS analyses, pts who did not die were censored at last structured activity. We used Cox proportional hazards models to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations between weekly time-varying state-level COVID-19 mortality rates and outcomes of rwPFS, rwTTP, and rwOS, adjusted for age at diagnosis, race/ethnicity, and state. Result(s): Among 7,813 advNSCLC pts, the median age at diagnosis was 70 years, the majority of the cohort was non-Hispanic White (59%), had non-squamous cell histology (68%) and a history of smoking (87%). Compared to pts living in states with the lowest quintile of COVID-19 mortality rates (Q1), pts living in states with the highest COVID-19 mortality (Q5) had worse rwOS (Q5 vs. Q1: HR 1.46, 95% CI 1.26-1.69) and rwPFS (Q5 vs. Q1: HR 1.18, 95% CI 1.05-1.33). No association was observed with rwTTP (Q5 vs. Q1: HR 1.05, 95% CI 0.90-1.22). Conclusion(s): In this study of real-world oncology data, we demonstrated the use of publicly-available COVID-19 mortality data to measure the time-varying impact of COVID-19 severity on outcomes in pts with advNSCLC. Higher state-level COVID-19 mortality rates were associated with worse rwOS and rwPFS among advNSCLC pts. The association with increased mortality among pts with advNSCLC may be related to COVID-19-related mortality or other factors such as pre-existing comorbidities which were not explored in this study.

Model design for nonparametric phylodynamic inference and applications to pathogen surveillance.

Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

Human activities at night negatively impact Little Penguin (Eudyptula minor) numbers and behaviours

The presence of humans within the natural environment is increasing worldwide. Assessing the impact of such activities on wildlife is crucial for declining populations where human disturbance adds to existing pressures. Here, we investigated how human activities at night influenced Little Penguin Eudyptula minor numbers and behaviours (specifically return time, number of vocalizations and time spent in vigilance) on Granite Island, a declining population in South Australia, Australia. We combined data from regular night surveys with continuous video and audio monitoring to assess the impact of human activities on the Little Penguins. The use of white light (i.e. from torches or camera flashes) by people was the most frequent activity recorded at night (recorded on 65% of the monitored nights). Fewer penguins were found on land at night when Dogs Canis lupus familiaris were present, but not when the number of people increased, when concerts occurred, or when white lights were used. Little Penguins were observed more often returning late from sea at night when Dogs were present and when white lights were used, but not when concerts occurred. An increase in penguin vocalizations at night correlated with the presence of Dogs and the occurrence of concerts, whereas penguins vocalized less when white lights were used. The time Little Penguins spent in vigilance did not correlate with any of the disturbances analysed. Our study also highlights the impact of coronavirus disease 2019 (COVID-19) on wildlife, as the occurrence of human activities increased significantly following the implementation of the COVID-19 health protection measures. These results add to a growing body of literature suggesting that human activities on land, and their consequent disturbance(s), may affect the numbers and behaviours of wildlife and that appropriate measures need to be developed to limit such impacts.

Fixed-length interval estimation of population sizes: sequential adaptive Monte Carlo mark–recapture–mark sampling

Mark–recapture sampling schemes are conventional approaches for population size (N) estimation. In this paper, we mainly focus on providing fixed-length confidence interval estimation methodologies for N under a mark–recapture–mark sampling scheme, where, during the resampling phase, non-marked items are marked before they are released back in the population. Using a Monte Carlo method, the interval estimates for N are obtained through a purely sequential procedure with an adaptive stopping rule. Such an adaptive decision criterion enables the user to "learn” with the subsequent marked and newly tagged items. The method is then compared with a recently developed accelerated sequential procedure in terms of coverage probability and expected number of captured items during the resampling stage. To illustrate, we explain how the proposed procedure could be applied to estimate the number of infected COVID-19 individuals in a near-closed population. In addition, we present a numeric application inspired on the problem of estimating the population size of endangered monkeys of the Atlantic forest in Brazil.

Global Trends in the Determination of Coronavirus PDB Protein Structures

This study aims to examine the international publication patterns of coronavirus protein database (PDB) structures, beginning when the first coronavirus virion PDB structures were published in 2002 to the present (2022). Sources of these depositions were extracted from their publications and used as indicators of how countries reacted to the pandemic through research output and were then compared to demographic and economic metrics. Of the approximately 40 countries examined, the United States, United Kingdom, and China had the highest number of proteins, demonstrating research productivity centeredness in highly developed countries. These countries all displayed a peak in protein depositions in 2020 or 2021, and slowed down in 2022 likely due to the peaking of the pandemic and a slowing necessity of response. Population size was found to contribute little to no factor in determining the number of coronavirus protein depositions while higher economic status measured by the GDP per capita did correlate with higher numbers of protein depositions (Jaffe et al, 2020). The number of confirmed Covid-19 cases showed a positive association with the number of PDB depositions per country, specifically in the United States. Furthermore, South Africa and Brazil, despite not being in the top 10 research-producing countries, had a high number of cases and PDB depositions, suggesting the strong impact of confirmed cases on coronavirus research output (Normile, 2022). This study's measure of how countries' economic status, population, and confirmed coronavirus cases affects their responses in terms of coronavirus protein research output suggests an unequal distribution in publication origins, which poses a challenge to global pandemic response coordination. This study continues an earlier study presented at the PDB50 - ASBMB on-line meeting, on May 4- 5, 2021 by Janet Gonzalez, Matthew K. McDevitt, David Roman, & Manfred Philipp. NA.Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.

Assessment of COVID-19 pandemic-related detrimental impact on the population of nuclear city: two-year results

Background - Since the announcement of the global coronavirus infection disease (COVID-19) pandemic, the attempts to assess the pandemic-related detrimental impact are of particular interest. The methodology of assessing the overall mortality attributed to COVID-19 pandemic, unlike the use of specific indicators that are sensitive to different methods of accounting the number of infected and deaths, provides more clear understanding of the pandemic-related impact. Objective - Quantitative assessment of the pandemic-related detrimental impact caused by the novel coronavirus infection in a small nuclear city, which is relevant for evaluating the effectiveness of anti-epidemic measures. Methods and Results - The population changes in a small urban district located in the South Ural Region of the Russian Federation were retrospectively analyzed for the decade, based on the open-source demographic data. The pandemic-related detrimental impact was calculated as overall excess mortality rate, compared with the previous non-pandemic years by using the additive model of excess absolute risk. The number of absolute excess deaths, adjusted for gender, age, population size, and number of diseased, was modeled using multivariate linear regression. The pandemic-related detriment was calculated based on the number of predicted excess deaths attributed to COVID-19. The relationship between the total number of deaths and the number of COVID-19 cases was analyzed. The total predicted two-year excess of pandemic-related deaths was 557.9. The pandemic-related total excess mortality per 1, 000 patients infected with SARSCov-2 was 50.2 (95% CI 38.4;62.0). Conclusion - The analyses revealed significant impact of the COVID-19 pandemic on the overall excess mortality in the nuclear city population in 2020 and 2021 implemented in both direct and indirect way. The population size was a major significant risk factor confounding the overall mortality. In order to develop an effective strategy to control and prevent the consequences of a pandemic, further monitoring of the epidemic situation in a nuclear city is required.Copyright © 2022, Russian Open Medical Journal.

Antecedents of Intention to Use E-Auction: An Empirical Study

Many public health organizations worldwide have used E-auctions to monitor, curtail, and improve the transmission of new coronavirus illnesses. However, user population size and acceptance of these technologies significantly impact their effectiveness. The current study's goal was to determine what factors influence customers' intent to use COVID-19 E-auctions by employing the Technology Acceptance Model (TAM) to the Jordanian setting. This study empirically assessed 310 Jordanian respondents using a quantitative approach known as Structural Equation Modeling (SEM). The research findings supported the majority of the proposed hypotheses, showing that behavioral intentions to use electronic bidding are highly influenced by perceived usability, perceived usefulness, trust in the government, social influence, and awareness. This research paper eventually contributes to the field of technology acceptance by developing a context-driven approach to the key pandemic components and features that influence different practices of technology acceptance. © 2023 by the authors.

Capture-Recapture Models with Heterogeneous Temporary Emigration

We propose a novel approach for modeling capture-recapture (CR) data on open populations that exhibit temporary emigration, while also accounting for individual heterogeneity to allow for differences in visit patterns and capture probabilities between individuals. Our modeling approach combines changepoint processes—fitted using an adaptive approach—for inferring individual visits, with Bayesian mixture modeling—fitted using a nonparametric approach—for identifying clusters of individuals with similar visit patterns or capture probabilities. The proposed method is extremely flexible as it can be applied to any CR dataset and is not reliant upon specialized sampling schemes, such as Pollock's robust design. We fit the new model to motivating data on salmon anglers collected annually at the Gaula river in Norway. Our results when analyzing data from the 2017, 2018, and 2019 seasons reveal two clusters of anglers—consistent across years—with substantially different visit patterns. Most anglers are allocated to the "occasional visitors” cluster, making infrequent and shorter visits with mean total length of stay at the river of around seven days, whereas there also exists a small cluster of "super visitors,” with regular and longer visits, with mean total length of stay of around 30 days in a season. Our estimate of the probability of catching salmon whilst at the river is more than three times higher than that obtained when using a model that does not account for temporary emigration, giving us a better understanding of the impact of fishing at the river. Finally, we discuss the effect of the COVID-19 pandemic on the angling population by modeling data from the 2020 season. Supplementary materials for this article are available online.

The Endangered Ganges River Dolphin (Platanista gangetica gangetica)

The iconic Ganges River dolphin Platanista gangetica gangetica is endemic to the Indian subcontinent and has been classified as the most endangered cetacean due to sharp decline in the population size of this obligatory freshwater animal. The species is vulnerable to multiple anthropogenic activities such as habitat fragmentation caused by construction of structural barriers (dams and barrages) and dredging activities, reduced freshwater flow, huge siltation load, depletion of fish stock, use of vulnerable gears, and lack of public awareness. Geographical expansion of artisanal fishing, poaching for collection of their flesh (used as fish bait) and fat and oil (used as an ointment for joint pain and gout), injuries and mortalities due to entanglement with fishing gear are other threats for the species. In addition, they can bioaccumulate several hazardous and toxic chemical pollutants in their body tissues, which are detrimental for their sustenance. Due to the outbreak of novel coronavirus (2019—COVID) pandemic the positive and negative impacts have also been observed and discussed. The following precautionary measures should be undertaken for their conservation: ban fishing in the dolphin hotspot areas and sanctuaries;multispecies management along with law enforcement and sustainable fishing practices;dolphin-fishery interactions should be solved through fishing gear modifications (e.g., mesh size). © 2022 Elsevier Inc. All rights reserved

Analysing the factors affecting medical waste generation in China

Medical waste deserves particular attention due to its potential for causing serious damage to people and the environment. Although the factors influencing the generation of medical waste are critical for designing policies aimed at effectively reducing medical waste and improving medical waste management, they have not been extensively studied. The rapid development of China's medical and health services and the sudden outbreak of Covid-19 have brought significant challenges to managing medical waste in China. Therefore, based on panel data from eight cities in China from 2013 to 2019, this study used a fixed-effects model to investigate the influencing factors of medical waste generation (MWG) in China, and tested the environmental Kuznets curve (EKC) hypothesis. The results show that there is a non-linear N-shaped curve relationship between MWG and per capita gross domestic product (GDP);MWG will continue to increase with economic growth, but the growth rate will slow down from fast to slow, and then from slow to fast with economic growth. The analysis also reveals that implementing a tiered diagnosis and treatment policy may negatively affect MWG by reducing the waste of medical resources and thus reducing the generation of medical waste. The positive effect of population size on MWG is also highly significant, so when the aging of the population increases, the generation of medical waste also increases. The three policy suggestions are provided: 1) improve the disposal capacity and efficiency of medical waste;2) give full play to the advantages of hierarchical diagnosis and treatment policy;3) improve the management level of medical waste in primary medical institutions. © 2023

Dependence-Robust Confidence Intervalsfor Capture-Recapture Surveys

Capture-recapture (CRC) surveys are used to estimate the size of a population whose members cannot be enumerated directly. CRC surveys have been used to estimate the number of Coronavirus Disease 2019 (COVID-19) infections, people who use drugs, sex workers, conflict casualties, and trafficking victims. When k-capture samples are obtained, counts of unit captures in subsets of samples are represented naturally by a 2k contingency table in which one element-the number of individuals appearing in none of the samples-remains unobserved. In the absence of additional assumptions, the population size is not identifiable (i.e., point identified). Stringent assumptions about the dependence between samples are often used to achieve point identification. However, real-world CRC surveys often use convenience samples in which the assumed dependence cannot be guaranteed, and population size estimates under these assumptions may lack empirical credibility. In this work, we apply the theory of partial identification to show that weak assumptions or qualitative knowledge about the nature of dependence between samples can be used to characterize a nontrivial confidence set for the true population size. We construct confidence sets under bounds on pairwise capture probabilities using two methods: test inversion bootstrap confidence intervals and profile likelihood confidence intervals. Simulation results demonstrate well-calibrated confidence sets for each method. In an extensive real-world study, we apply the new methodology to the problem of using heterogeneous survey data to estimate the number of people who inject drugs in Brussels, Belgium.

Estimating the causal effect of mobility on Dengue during the COVID-19 pandemic

Background: The COVID-19 pandemic led to a reduction in human mobility which occurred randomly (in time) and is not linked to any other Dengue risk factors. This gives rise to a quasi-experimental situation to assess the impact of mobility reduction on Dengue Fever in Brazilian cities using propensity score matching. Method(s): We match weeks during the peak pandemic period for 37 cities in Sao Paulo state with comparable prior periods based on instruments for mosquito population size and human susceptibility. By matching within cities, we also control for city-level characteristics, such as landscape or population density. We compute propensity scores using logistic regression and Random Forests and implement both one-to-one and one-to-many matching with calipers. Result(s): We compare the Sample Average Treatment Effect on the Treated (SATT) across models and find variation in the direction of the causal effect. In 12 cities, mobility reductions are linked to more Dengue cases, while fewer cases are reported in 9 cities. The remaining cities are sensitive to the model chosen. Conclusion(s): The SATT of mobility on Dengue varies across the cities in our sample, with more cities experiencing an increase in cases during the COVID-19 pandemic. Key messages: A quasi-experimental analysis suggests that there is a a causal effect of mobility on Dengue that varies across cities in Sao Paulo state.

PREDICTORS OF SPONTANEOUS PNEUMOMEDIASTINUM IN PATIENTS WITH COVID-19 AND ARDS ON HIGH-FLOW NASAL CANNULA

SESSION TITLE: ECMO and ARDS in COVID-19 Infections SESSION TYPE: Rapid Fire Original Inv PRESENTED ON: 10/17/2022 12:15 pm - 1:15 pm PURPOSE: High Flow Nasal Cannula (HFNC) is a non-invasive ventilation (NIV) device widely used to manage hypoxemic respiratory failure. Data about optimal flow rate and time length for safety is lacking. Cases of spontaneous pneumomediastinum (SP) during HFNC oxygen therapy in COVID-19 patients have been recently reported. A study in airway models suggests a non-linear increase in PEEP up to 10 cmH2O in adults on maximum tested flows. Prolonged use of NIV could also delay escalation to invasive ventilation and use of lung-protective volumes (LPV). The ROX-index is a predictive tool for NIV failure and continuous evaluation of intubation indications. This study aimed to identify risk factors associated with the development of SP in COVID-19 ARDS on HFNC support and establish mitigating behavior that will aid in safer COVID-19 treatment modalities. METHODS: Cases from 2020 to 2022 were reviewed. Patients with SP while on HFNC were included as cases. Age and gender-matched patients who received HFNC and did not develop SP were controls. Baseline characteristics between groups were compared using t-test for continuous variables and chi-square for categorical values. Longitudinal ROX scores were calculated until the last day (day of pneumomediastinum development for SP group, and death or MV commencement for controls). Nominal logistic regression was performed to identify variables associated with SP development. Parameter Estimates were used to construct a prediction model, and a ROC curve was implemented to assess the accuracy of the prediction of SP events. RESULTS: Total 61 patients enrolled, 52% (32/61) developed SP on HFNC and 48% (29/61) were control group (CG). No statistical significance found on baseline demographics. Median HFNC days-to-SP was 7 [standard deviation (SD), 6.8 days]. Median days from COVID-19 diagnosis-to-SP was 9 (SD, 5 days). Use of MV was greater in SP group (29 vs 3, p-value < 0.001) and use of vassopresor support (28 vs 3, p-value < 0.001). SP-group had an increased mortality compared to CG, with 88% (28/32) vs.12% (3) (p-value, <0.001). Median ROX scores on Day 1 were 5.45 for SP group and 18.2 for CG (p<0.001). Median ROX scores on last day (day-to-event) were 4.08 and 9.4 in CG (p<0.001). Nominal logistic regression identified number of days on HFNC, ROX score on day 1, and cumulative amount of Flow rate, as independent variables associated with SP development. ROC of the Prediction model using parameter estimates from these 3 variables had an AUC of 0.922. CONCLUSIONS: Development of SP is associated with increased mortality. Patients with lower ROX scores at initiation of therapy, prolonged days of HFNC and increased cumulative flow rates are associated with the development of SP. CLINICAL IMPLICATIONS: HFNC has the potential to cause alveolar damage, however a larger patient population size is needed to further analyze the relationship of HFNC use and the development of SP. DISCLOSURES: No relevant relationships by Sofia Durscki Vianna No relevant relationships by Cynthia Espinosa No relevant relationships by Hernando Garcia No relevant relationships by Ephraim Mansour No relevant relationships by Laura Mendez Morente No relevant relationships by Zuleikha Muzaffarr No relevant relationships by Sergio Poli No relevant relationships by Luisa Quesada No relevant relationships by Douglas Salguero No relevant relationships by Michelle Yousefzadeh

Capture-recapture models with heterogeneous temporary emigration

We propose a novel approach for modelling capture-recapture (CR) data on open populations that exhibit temporary emigration, whilst also accounting for individual heterogeneity to allow for differences in visit patterns and capture probabilities between individuals. Our modelling approach combines changepoint processes – fitted using an adaptive approach – for inferring individual visits, with Bayesian mixture modelling – fitted using a nonparametric approach – for identifying clusters of individuals with similar visit patterns or capture probabilities. The proposed method is extremely flexible as it can be applied to any CR data set and is not reliant upon specialised sampling schemes, such as Pollock’s robust design. We fit the new model to motivating data on salmon anglers collected annually at the Gaula river in Norway. Our results when analysing data from the 2017, 2018 and 2019 seasons reveal two clusters of anglers – consistent across years – with substantially different visit patterns. Most anglers are allocated to the “occasional visitors” cluster, making infrequent and shorter visits with mean total length of stay at the river of around seven days, whereas there also exists a small cluster of “super visitors”, with regular and longer visits, with mean total length of stay of around 30 days in a season. Our estimate of the probability of catching salmon whilst at the river is more than three times higher than that obtained when using a model that does not account for temporary emigration, giving us a better understanding of the impact of fishing at the river. Finally, we discuss the effect of the COVID-19 pandemic on the angling population by modelling data from the 2020 season. Supplementary materials for this article are available online. [ FROM AUTHOR] Copyright of Journal of the American Statistical Association is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

MATHEMATICAL MODELING OF THE VACCINATION INFLUENCE ON THE COVID-19 EPIDEMIC PROPAGATION

The mathematical model based on a system of ordinary differential equations is proposed to describe the effect of the vaccination rate on the spread of the COVID-19 epidemic. The results of numerical modeling are presented for the case when vaccination begins after the beginning of the epidemic. A dimensionless vaccination parameter V was obtained, which allows one to characterize the effect of the vaccination rate on the reduction of the incidence of viral diseases with different virulence levels in a large closed population of people. Introducing this parameter allows the simulation results to be generalized to the populations of different size, different epidemic spread rate, different vaccination rate, and different vaccine efficiency. It has been shown that increasing the parameter V decreases the proportion of the sick population. It follows from our model that the vaccination influence on the spread of a respiratory viral disease such as COVID-19 decreases for a later initiation of vaccination. The simulation results should contribute to the development of optimal vaccination scenarios for the population.

Tracking the molecular evolution and transmission patterns of SARS-CoV-2 lineage B.1.466.2 in Indonesia based on genomic surveillance data.

BACKGROUND: As a new epi-center of COVID-19 in Asia and a densely populated developing country, Indonesia is facing unprecedented challenges in public health. SARS-CoV-2 lineage B.1.466.2 was reported to be an indigenous dominant strain in Indonesia (once second only to the Delta variant). However, it remains unclear how this variant evolved and spread within such an archipelagic nation. METHODS: For statistical description, the spatiotemporal distributions of the B.1.466.2 variant were plotted using the publicly accessible metadata in GISAID. A total of 1302 complete genome sequences of Indonesian B.1.466.2 strains with high coverage were downloaded from the GISAID's EpiCoV database on 28 August 2021. To determine the molecular evolutionary characteristics, we performed a time-scaled phylogenetic analysis using the maximum likelihood algorithm and called the single nucleotide variants taking the Wuhan-Hu-1 sequence as reference. To investigate the spatiotemporal transmission patterns, we estimated two dynamic parameters (effective population size and effective reproduction number) and reconstructed the phylogeography among different islands. RESULTS: As of the end of August 2021, nearly 85% of the global SARS-CoV-2 lineage B.1.466.2 sequences (including the first one) were obtained from Indonesia. This variant was estimated to account for over 50% of Indonesia's daily infections during the period of March-May 2021. The time-scaled phylogeny suggested that SARS-CoV-2 lineage B.1.466.2 circulating in Indonesia might have originated from Java Island in mid-June 2020 and had evolved into two disproportional and distinct sub-lineages. High-frequency non-synonymous mutations were mostly found in the spike and NSP3; the S-D614G/N439K/P681R co-mutations were identified in its larger sub-lineage. The demographic history was inferred to have experienced four phases, with an exponential growth from October 2020 to February 2021. The effective reproduction number was estimated to have reached its peak (11.18) in late December 2020 and dropped to be less than one after early May 2021. The relevant phylogeography showed that Java and Sumatra might successively act as epi-centers and form a stable transmission loop. Additionally, several long-distance transmission links across seas were revealed. CONCLUSIONS: SARS-CoV-2 variants circulating in the tropical archipelago may follow unique patterns of evolution and transmission. Continuous, extensive and targeted genomic surveillance is essential.

The Importance of Scaling for an Agent Based Model: An Illustrative Case Study with COVID-19 in Zimbabwe

Agent-based models frequently make use of scaling techniques to render the simulated samples of population more tractable. The degree to which this scaling has implications for model forecasts, however, has yet to be explored;in particular, no research on the spatial implications of this has been done. This work presents a simulation of the spread of Covid-19 among districts in Zimbabwe and assesses the extent to which results vary relative to the samples upon which they are based. It is determined that in particular, different geographical dynamics of the spread of disease are associated with varying population sizes, with implications for others seeking to use scaled populations in their research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.