Epidemic control by social distancing and vaccination: Optimal strategies and remarks on the COVID-19 Italian response policy.

After the many failures in the control of the COVID-19 pandemic, identifying robust principles of epidemic control will be key in future preparedness. In this work, we propose an optimal control model of an age-of-infection transmission model under a two-phase control regime where social distancing is the only available control tool in the first phase, while the second phase also benefits from the arrival of vaccines. We analyzed the problem by an ad-hoc numerical algorithm under a strong hypothesis implying a high degree of prioritization to the protection of health from the epidemic attack, which we termed the "low attack rate" hypothesis. The outputs of the model were also compared with the data from the Italian COVID-19 experience to provide a crude assessment of the goodness of the enacted interventions prior to the onset of the Omicron variant.

Effect of vaccinations and school restrictions on the spread of COVID-19 in different age groups in Germany.

With the emergence of SARS-CoV-2, various non-pharmaceutical interventions were adopted to control virus transmission, including school closures. Subsequently, the introduction of vaccines mitigated not only disease severity but also the spread of SARS-CoV-2. This study leveraged an adapted SIR model and non-linear mixed-effects modeling to quantify the impact of remote learning, school holidays, the emergence of Variants of Concern (VOCs), and the role of vaccinations in controlling SARS-CoV-2 spread across 16 German federal states with an age-stratified approach. Findings highlight a significant inverse correlation (Spearman's ρ = -0.92, p < 0.001) between vaccination rates and peak incidence rates across all age groups. Model-parameter estimation using the observed number of cases stratified by federal state and age allowed to assess the effects of school closure and holidays, considering adjustments for vaccinations and spread of VOCs over time. Here, modeling revealed significant (p < 0.001) differences in the virus's spread among pre-school children (0-4), children (5-11), adolescents (12-17), adults (18-59), and the elderly (60+). The transition to remote learning emerged as a critical measure in significantly reducing infection rates among children and adolescents (p < 0.001), whereas an increased infection risk was noted among the elderly during these periods, suggesting a shift in infection networks due to altered caregiving roles. Conversely, during school holiday periods, infection rates among adolescents mirrored those observed when schools were open. Simulation exercises based on the model provided evidence that COVID-19 vaccinations might serve a dual purpose: they protect the vaccinated individuals and contribute to the broader community's safety.

Epidemiology and viral loads of respiratory syncytial virus in hospitalized children prior to and during COVID-19 pandemic in Hangzhou, China.

Non-pharmaceutical interventions (NPIs) implemented to control SARS-CoV-2 have significantly influenced the activity of respiratory pathogens. This study investigated epidemiological changes among hospitalized patients with respiratory syncytial virus (RSV) before (2017-2019) and during (2020-2022) the COVID-19 pandemic in Hangzhou, China. We also examined viral load distribution across demographic and temporal variables. Nasopharyngeal swabs were collected and RSV loads were quantified using reverse transcriptase polymerase chain reaction (RT-qPCR). RSV epidemic characteristics, seasonal dynamics, and viral load distributions were compared between pre- and pandemic years. General linear models were employed to assess associations between viral loads and age. Among 19 742 cases, 1576 and 2092 tested positive during the pre- and pandemic years, respectively. From February to July 2020, the implementation of NPIs led to the cessation of RSV circulation. However, after these measures were relaxed, RSV cases resurged over two consecutive seasons during the pandemic, notably affecting older children compared to those in the pre-pandemic years (1.00 years, IQR: 0.50-2.00 vs. 0.58 years, IQR: 0.27-1.00, p < 0.001). Specifically, in 2021-2022, an off-season resurgence of RSV began earlier (mid-June), lasted longer (40 weeks), and involved more positive cases (1238 cases) than both 2020-2021 and pre-pandemic years. Viral load distribution demonstrated a clear age-related relationship in both pre- and pandemic years, with younger children consistently showing higher viral loads, independently of gender and season (all p-values for trends <0.001). These findings highlight the impact of NPIs on RSV epidemiology and underscore the need to prioritize RSV infection prevention in younger children from the perspective of viral load.

Understanding the secondary outcomes of international travel measures during the covid-19 pandemic: a scoping review of social impact evidence.

BACKGROUND: Assessment of the effective use of international travel measures during the COVID-19 pandemic has focused on public health goals, namely limiting virus introduction and onward transmission. However, risk-based approaches includes the weighing of public health goals against potential social, economic and other secondary impacts. Advancing risk-based approaches thus requires fuller understanding of available evidence on such impacts. METHODS: We conducted a scoping review of existing studies of the social impacts of international travel measures during the COVID-19 pandemic. Applying a standardized typology of travel measures, and five categories of social impact, we searched 9 databases across multiple disciplines spanning public health and the social sciences. We identified 26 studies for inclusion and reviewed their scope, methods, type of travel measure, and social impacts analysed. RESULTS: The studies cover a diverse range of national settings with a strong focus on high-income countries. A broad range of populations are studied, hindered in their outbound or inbound travel. Most studies focus on 2020 when travel restrictions were widely introduced, but limited attention is given to the broader effects of their prolonged use. Studies primarily used qualitative or mixed methods, with adaptations to comply with public health measures. Most studies focused on travel restrictions, as one type of travel measure, often combined with domestic public health measures, making it difficult to determine their specific social impacts. All five categories of social impacts were observed although there was a strong emphasis on negative social impacts including family separation, decreased work opportunities, reduced quality of life, and inability to meet cultural needs. A small number of countries identified positive social impacts such as restored work-life balance and an increase in perceptions of safety and security. CONCLUSIONS: While international travel measures were among the most controversial interventions applied during the COVID-19 pandemic, given their prolonged use and widespread impacts on individuals and populations, there remains limited study of their secondary impacts. If risk-based approaches are to be advanced, involving informed choices between public health and other policy goals, there is a need to better understand such impacts, including their differential impacts across diverse populations and settings.

Evaluation of the effectiveness of Washington State's digital COVID-19 exposure notification system over one pandemic year.

Introduction: Digital exposure notifications are a novel public health intervention used during the COVID-19 pandemic to alert users of possible COVID-19 exposure. We seek to quantify the effectiveness of Washington State's digital exposure notification system, WA Notify, as measured by the number of COVID-19 cases averted during a 1-year period. Methods: While maintaining individuals' privacy, WA Notify collected data that could be used to evaluate the system's effectiveness. This article uses these and other data and builds on a previous model to estimate the number of cases averted by WA Notify. Novel estimates of some model parameters are possible because of improvements in the quality and breadth of data reported by WA Notify. Results: We estimate that WA Notify averted 64,000 (sensitivity analysis: 35,000-92,000) COVID-19 cases in Washington State during the study period from 1 March 2021 to 28 February 2022. During this period, there were an estimated 1,089,000 exposure notifications generated and 155,000 cases reported to WA Notify. During the last 78 days of the study period, the median estimated number of daily active users was 1,740,000. Discussion: We believe WA Notify reduced the impact of the COVID-19 pandemic in Washington State and that similar systems could reduce the impact of future communicable disease outbreaks.

Impact of non-pharmaceutical interventions on COVID-19 incidence and deaths: cross-national natural experiment in 32 European countries.

PURPOSE: Non-pharmaceutical interventions (NPIs) have been the cornerstone of COVID-19 pandemic control, but evidence on their effectiveness varies according to the methods and approaches taken to empirical analysis. We analysed the impact of NPIs on incident SARS-CoV-2 across 32 European countries (March-December 2020) using two NPI trackers: the Corona Virus Pandemic Policy Monitor - COV-PPM, and the Oxford Covid-19 Government Response Tracker - OxCGRT. METHODS: NPIs were summarized through principal component analysis into three sets, stratified by two waves (C1-C3, weeks 5-25, and C4-C6, weeks 35-52). Longitudinal, multi-level mixed-effects negative binomial regression models were fitted to estimate incidence rate ratios for cases and deaths considering different time-lags and reverse causation (i.e. changing incidence causing NPIs), stratified by waves and geographical regions (Western, Eastern, Northern, Southern, Others). RESULTS: During the first wave, restrictions on movement/mobility, public transport, public events, and public spaces (C1) and healthcare system improvements, border closures and restrictions to public institutions (C2) were associated with a reduction in SARS-CoV-2 incidence after 28 and 35-days. Mask policies (C3) were associated with a reduction in SARS-CoV-2 incidence (except after 35-days). During wave 1, C1 and C2 were associated with a decrease in deaths after 49-days and C3 after 21, 28 and 35-days. During wave 2, restrictions on movement/mobility, public transport and healthcare system improvements (C5) were also associated with a decrease in SARS-CoV-2 cases and deaths across all countries. CONCLUSION: In the absence of pre-existing immunity, vaccines or treatment options, our results suggest that the observed implementation of different categories of NPIs, showed varied associations with SARS-CoV-2 incidence and deaths across regions, and varied associations across waves. These relationships were consistent across components of NPIs derived from two policy trackers (CoV-PPM and OxCGRT).

Lifting COVID-19-associated non-pharmaceutical interventions: potential impact on notifications of infectious diseases transmitted from person to person in 2022 in Bavaria, Germany.

Background: The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) have led to substantial decreases in case numbers of infectious diseases in several countries worldwide. As NPIs were gradually lifted, intense or out-of-season outbreaks of respiratory and gastrointestinal diseases were reported, raising the hypothesis of a potential catch-up effect of infections. By analysing surveillance data from the federal reporting system for notifiable infectious diseases, we aimed to assess the potential impact of lifting COVID-19 associated NPIs on notifications of selected infectious diseases in Bavaria, 2022. Methods: We compared influenza, chickenpox, norovirus gastroenteritis, rotavirus gastroenteritis weekly case numbers in a pre-pandemic period (2016-2019) and 2022 using two time series analyses approaches: (i) a predictive model forecasting weekly case numbers for the pandemic years 2020-2022, based on 2016-2019 data, (ii) interrupted time series model, based on 2016-2022 data, including a term per pandemic period. Results: In 2022, incidence rates were higher compared to pre-pandemic period for influenza (IRR = 3.47, 95%CI: 1.49-7.94) and rotavirus gastroenteritis (IRR = 1.36, 95%CI: 0.95-1.93), though not significant for rotavirus gastroenteritis. Conversely, case numbers remained significantly below pre-pandemic levels for chickenpox (IRR = 0.52, 95%CI: 0.41-0.65) and norovirus gastroenteritis (IRR = 0.59, 95%CI: 0.42-0.82). Seasonality changed notably for influenza, showing an earlier influenza wave compared to pre-pandemic periods. Conclusion: The lifting of NPIs was associated with heterogenic epidemiological patterns depending on the selected disease. The full impact of NPIs and their discontinuation may only become clear with continued monitoring and assessment of potential additional contributing factors.

COVID-19 prevention and control effect of non-pharmaceutical interventions-fuzzy-sets qualitative comparative analysis based on 69 countries in the world.

Introduction: Coronavirus disease 2019 occurred unexpectedly in late December 2019, it was difficult to immediately develop an effective vaccine or propose targeted medical interventions in the early stages of the outbreak. At this point, non-pharmaceutical interventions (NPIs) are essential components of the public health response to COVID-19. How to combine different NPIs in the early stages of an outbreak to control the spread of epidemics and ensure that the policy combination does not incur high socio-economic costs became the focus of this study. Methods: We mainly used the fuzzy set qualitative comparative analysis to assess the impact of different combinations of NPIs on the effectiveness of control in the COVID-19 pandemic early stage, using open datasets containing case numbers, country populations and policy responses. Results: We showed that the configuration of high morbidity results includes one, which is the combination of non-strict face covering, social isolation and travel restrictions. The configuration of non-high morbidity results includes three, one is strict mask wearing measures, which alone constitute sufficient conditions for interpreting the results; the second is strict testing and contact tracing, social isolation; the third is strict testing and contact tracing, travel restriction. The results of the robustness test showed that the number, components and consistency of the configurations have not changed after changing the minimum case frequency, which proved that the analysis results are reliable. Conclusion: In the early stages of the epidemic, the causes of high morbidity are not symmetrical with the causes of non-high morbidity. Strict face covering is the most basic measure required to prevent and control epidemics, and the combination of non-strict face covering and containment is the most important factor leading to poor prevention and control, and the combination of strict containment and proactive pursuit is the way to achieve superior prevention and control, timely and proactive containment strategies have better prevention and control, and should mobilize the public to cooperate.

Absenteeism and Health Behavior Trends Associated With Acute Respiratory Illness Before and During the COVID-19 Pandemic in a Community Household Cohort, King County, Washington.

Introduction: Longitudinal data on how acute respiratory illness (ARI) affects behavior, namely school or work participation, and nonpharmaceutical intervention (NPI) usage before and during the COVID-19 pandemic is limited. The authors assessed how ARIs and specific symptoms affected school, work, and health-related behaviors over time. Methods: From November 2019 to June 2021, participating households with children in King County, Washington, were remotely monitored for ARI symptoms weekly. Following ARIs, participants reported illness-related effects on school, work, and NPI use. Using logistic regression with generalized estimating equations, the authors examined associations between symptoms and behaviors. Results: Of 1,861 participants, 581 (31%) from 293 households reported 884 ARIs and completed one-week follow-up surveys. Compared with the prepandemic period, during the period of the pandemic pre-COVID-19 vaccine, ARI-related school (56% vs 10%, p<0.001) absenteeism decreased and masking increased (3% vs 28%, p<0.001). After vaccine authorization in December 2020, more ARIs resulted in masking (3% vs 48%, p<0.001), avoiding contact with non-household members (26% vs 58%, p<0.001), and staying home (37% vs 69%, p<0.001) compared with the prepandemic period. Constitutional symptoms such as fever were associated with work disruptions (OR=1.91; 95% CI=1.06, 3.43), staying home (OR=1.55; 95% CI=1.06, 2.27), and decreased contact with non-household members (OR=1.58; 95% CI=1.05, 2.36). Conclusions: This remote household study permitted uninterrupted tracking of behavioral changes in families with children before and during the COVID-19 pandemic, identifying increased use of some NPIs when ill but no additional illness-associated work or school disruptions.

Optimal approaches for COVID-19 control: the use of vaccines and lockdowns across societal groups.

Background: By March 2023, the COVID-19 illness had caused over 6.8 million deaths globally. Countries restricted disease spread through non-pharmaceutical interventions (NPIs; e.g. social distancing). More severe "lockdowns" were also required to manage disease spread. Although lockdowns effectively reduce virus transmission, they substantially disrupt economies and individual well-being. Fortunately, the availability of vaccines provides alternative approaches to manage disease spread. Yet, vaccination programs take several months to implement fully, require further time for individuals to develop immunity following inoculation, may not have complete coverage and/or may be imperfectly efficacious against the virus. Given these aspects of a vaccination programme, it is important to understand how NPIs (such as lockdowns) can be used in conjunction with vaccination to achieve public health goals. Methods: We use mathematical methods to, investigate optimal approaches for vaccination under varying lockdown lengths and/or severities to prevent COVID-19-related deaths exceeding critical thresholds. Results: We find that increases in vaccination rate cause a disproportionate decrease in the length and severity lockdowns to keep mortality levels below a critical threshold. With vaccination, severe lockdowns can further reduce infections by up to 89%. Notably, we include simple demographics, modelling three groups: vulnerable, front-line workers, and non-vulnerable. We investigate the sequence of vaccination. One counter-intuitive finding is that even though the vulnerable group is high risk, demographically, this is a small group and critically, per person, vaccination therefore occurs more slowly. Hence vaccinating this group first achieves limited gains in overall disease control. Discussion: Importantly, we conclude that improved disease control may be best achieved by vaccinating the non-vulnerable group coupled with longer and/or more severe NPIs.

Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study.

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.

By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.

Influence of non-pharmaceutical interventions during the COVID-19 pandemic on respiratory viral infections - a prospective population-based cohort study.

Non-pharmaceutical interventions (NPI) have been proven successful in a population-based approach to protect from SARS-CoV-2 transmission during the COVID-19 pandemic. As a consequential-effect, a reduction in the spread of all respiratory viruses has been observed, but the primary factors behind this phenomenon have yet to be identified. We conducted a subgroup analysis of participants from the ELISA study, a prospective longitudinal cohort study on SARS-CoV-2 transmission, at four timepoints from November 2020 - September 2022. The aim was to provide a detailed overview of the circulation of respiratory viruses over 2 years and to identify potential personal risk factors of virus distribution. All participants were screened using qPCR for respiratory viral infections from nasopharyngeal swabs and answered a questionnaire regarding behavioral factors. Several categories of risk factors for the transmission of respiratory viruses were evaluated using a scoring system. In total, 1,124 participants were included in the study, showing high adherence to governmental-introduced NPI. The overall number of respiratory virus infections was low (0-4.9% of participants), with adenovirus (1.7%), rhino-/enterovirus (3.2%) and SARS-CoV-2 (1.2%) being the most abundant. We detected an inverse correlation between the number and intensity of NPI and the number of detected respiratory viruses. More precisely, the attendance of social events and household size was associated with rhino-/enterovirus infection while social contacts were associated with being positive for any virus. NPI introduced during the COVID-19 pandemic reduced the occurrence of seasonal respiratory viruses in our study, showing different risk-factors for enhanced transmission between viruses. Trial registration: DRKS.de, German Clinical Trials Register (DRKS), Identifier: DRKS00023418, Registered on 28 October 2020.

Impact of COVID-19 Pandemic Restrictions on Respiratory Virus Patterns: Insights from RSV Surveillance in Gwangju, South Korea.

The social restriction measures implemented due to the COVID-19 pandemic have impacted the pattern of occurrences of respiratory viruses. According to surveillance results in the Gwangju region of South Korea, respiratory syncytial virus (RSV) did not occur during the 2020/2021 season. However, there was a delayed resurgence in the 2021/2022 season, peaking until January 2022. To analyze this, a total of 474 RSV positive samples were investigated before and after the COVID-19 pandemic. Among them, 73 samples were selected for whole-genome sequencing. The incidence rate of RSV in the 2021/2022 season after COVID-19 was found to be approximately three-fold higher compared to before the pandemic, with a significant increase observed in the age group from under 2 years old to under 5 years old. Phylogenetic analysis revealed that, for RSV-A, whereas four lineages were observed before COVID-19, only the A.D.3.1 lineage was observed during the 2021/2022 season post-pandemic. Additionally, during the 2022/2023 season, the A.D.1, A.D.3, and A.D.3.1 lineages co-circulated. For RSV-B, while the B.D.4.1.1 lineage existed before COVID-19, both the B.D.4.1.1 and B.D.E.1 lineages circulated after the pandemic. Although atypical RSV occurrences were not due to new lineages, there was an increase in the frequency of mutations in the F protein of RSV after COVID-19. These findings highlight the need to continue monitoring changes in RSV occurrence patterns in the aftermath of the COVID-19 pandemic to develop and manage strategies in response.

Survey of public knowledge, attitudes, and practices regarding personal protection against COVID-19 in the post-pandemic era.

In the emerging post-pandemic era (the 'wavelet' era), humans must coexist with viruses for the foreseeable future, and personal protective behaviors will largely replace national-level preventive measures. In this new normal, encouraging the public to implement proper personal protective behaviors against the coronavirus disease (COVID-19) is vital to the sustainable development of cities and communities. This knowledge-attitude-practice (KAP) survey conducted in Chengdu (N = 900) narrowed the knowledge gap regarding post-pandemic public practices of protective behavior. Findings show that:(1) approximately 1/3 of the respondents are currently not concerned about COVID-19 at all; (2) respondents with different demographics and individual COVID-19-related factors showed significant differences in practice behaviors indoors and outdoors; (3) vulnerable groups performed better in practice behavior indoors/outdoors; (4) because the public may relax their vigilance outdoors, public places may become a transmission threat in the next outbreak; (5) attitudes are important, but limited incentives for practice; and (6) when knowledge increases beyond a threshold (68.75-75% in this study), protective behaviors decrease. Our results suggest that authorities must continue to educate and motivate the public, extending measures to cover personal protective practices, and have targeted policies for specific demographics to ensure equity in healthcare in the event of another pandemic (COVID-19 and alike crisis). Besides, comparing the results of the current study with similar studies conducted in other parts of the world can provide insights into how different populations respond to and adopt COVID-19 protective behaviors. The epidemiologists can use the data collected by this and other KAP surveys to refine epidemiologic models, which can help predict the spread of the virus and the impact of interventions in different settings.

Effects of coronavirus disease 2019 on the spread of respiratory-transmitted human-to-human bacteria.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has necessitated significant changes in medical systems, social behaviours, and non-pharmaceutical interventions (NPIs). We aimed to determine the effect of the COVID-19 pandemic on changes in the epidemiology of respiratory-transmitted bacteria that have been unexplored. METHODS: We utilised a comprehensive national surveillance database from 2018 to 2021 to compare monthly number of patients with four respiratory-transmitted human-to-human bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pyogenes) before and after the COVID-19 pandemic, stratified by specimen sources and age groups. RESULTS: The incidence of detected patients with S. pneumoniae, H. influenzae, and S. pyogenes from both respiratory and blood cultures significantly decreased from 2019 to 2020. In 2021, the incidence of detected patients with the respiratory-transmitted bacterial species, except for S. pyogenes, from respiratory cultures, increased again from April to July, primarily affecting the 0-4-year age group. CONCLUSIONS: Our comprehensive national surveillance data analysis demonstrates the dynamic changes and effects of NPIs on respiratory-transmitted bacteria during the COVID-19 pandemic, with variations observed among species, specimen sources, and age groups.

Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic.

Background: The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. Methods: We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Results: Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)-third quartile (Q3) = 94-108%) of typical usage to 10% (Q1-Q3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(ß) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number (ß = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Discussion: Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges.

COVID-19 outbreaks in residential aged care facilities: an agent-based modeling study.

Introduction: A disproportionate number of COVID-19 deaths occur in Residential Aged Care Facilities (RACFs), where better evidence is needed to target COVID-19 interventions to prevent mortality. This study used an agent-based model to assess the role of community prevalence, vaccination strategies, and non-pharmaceutical interventions (NPIs) on COVID-19 outcomes in RACFs in Victoria, Australia. Methods: The model simulated outbreaks in RACFs over time, and was calibrated to distributions for outbreak size, outbreak duration, and case fatality rate in Victorian RACFs over 2022. The number of incursions to RACFs per day were estimated to fit total deaths and diagnoses over time and community prevalence.Total infections, diagnoses, and deaths in RACFs were estimated over July 2023-June 2024 under scenarios of different: community epidemic wave assumptions (magnitude and frequency); RACF vaccination strategies (6-monthly, 12-monthly, no further vaccines); additional non-pharmaceutical interventions (10, 25, 50% efficacy); and reduction in incursions (30% or 60%). Results: Total RACF outcomes were proportional to cumulative community infections and incursion rates, suggesting potential for strategic visitation/staff policies or community-based interventions to reduce deaths. Recency of vaccination when epidemic waves occurred was critical; compared with 6-monthly boosters, 12-monthly boosters had approximately 1.2 times more deaths and no further boosters had approximately 1.6 times more deaths over July 2023-June 2024. Additional NPIs, even with only 10-25% efficacy, could lead to a 13-31% reduction in deaths in RACFs. Conclusion: Future community epidemic wave patterns are unknown but will be major drivers of outcomes in RACFs. Maintaining high coverage of recent vaccination, minimizing incursions, and increasing NPIs can have a major impact on cumulative infections and deaths.

Non-pharmaceutical interventions in containing COVID-19 pandemic after the roll-out of coronavirus vaccines: a systematic review.

BACKGROUND: Non-pharmaceutical interventions (NPIs) have been widely utilised to control the COVID-19 pandemic. However, it is unclear what the optimal strategies are for implementing NPIs in the context of coronavirus vaccines. This study aims to systematically identify, describe, and evaluate existing ecological studies on the real-world impact of NPIs in containing COVID-19 pandemic following the roll-out of coronavirus vaccines. METHODS: We conducted a comprehensive search of relevant studies from January 1, 2021, to June 4, 2023 in PubMed, Embase, Web of science and MedRxiv. Two authors independently assessed the eligibility of the studies and extracted the data. A risk of bias assessment tool, derived from a bibliometric review of ecological studies, was applied to evaluate the study design, statistical methodology, and the quality of reporting. Data were collected, synthesised and analysed using qualitative and quantitative methods. The results were presented using summary tables and figures, including information on the target countries and regions of the studies, types of NPIs, and the quality of evidence. RESULTS: The review included a total of 17 studies that examined the real-world impact of NPIs in containing the COVID-19 pandemic after the vaccine roll-out. These studies used five composite indicators that combined multiple NPIs, and examined 14 individual NPIs. The studies had an average quality assessment score of 13 (range: 10-16), indicating moderately high quality. NPIs had a larger impact than vaccination in mitigating the spread of COVID-19 during the early stage of the vaccination implementation and in the context of the Omicron variant. Testing policies, workplace closures, and restrictions on gatherings were the most effective NPIs in containing the COVID-19 pandemic, following the roll-out of vaccines. The impact of NPIs varied across different time frames, countries and regions. CONCLUSION: NPIs had a larger contribution to the control of the pandemic as compared to vaccination during the early stage of vaccine implementation and in the context of the omicron variant. The impact of NPIs in containing the COVID-19 pandemic exhibited variability in diverse contexts. Policy- and decision-makers need to focus on the impact of different NPIs in diverse contexts. Further research is needed to understand the policy mechanisms and address potential future challenges.