A Documentary History of the Immunity (or Vaccine) Passport: Health Certificates of Public Health, Personal Identity and Power from the Plague to the Coronavirus Pandemic

The immunity (or vaccine) passport of the coronavirus pandemic, as a concept and object, is not unprecedented. This health and identity document features a history spanning over half-a-millennium and appearing across diverse geopolitical and sociocultural contexts. This article presents a documentary history of the immunity passport and its heterogeneous material instantiations, uses and effects across divergent historical settings. It illuminates how the immunity passport has helped shaped identities and public health, as well as impacted individual and institutional agency, during health crises. Four historical cases are explored, including the plagues ravaging the Renaissance Mediterranean region, the 1665 Great Plague of London, the yellow fever outbreaks in the antebellum slave-era southern USA and the chronic cholera conditions confronting colonial-era British India. Although disparate, these historical cases share the immunity passport as a non-pharmaceutical intervention into their respective health crises that played important roles in people's lives during these troubled times.

The Role of "Hierarchical and Classified Prevention and Control Measures (HCPC)" Strategy for SARS-CoV-2 Delta Variant in Guangzhou: A Modeling Study.

BACKGROUND: The Delta variant of SARS-COV-2 has replaced previously circulating strains around the world in 2021. Sporadic outbreaks of the Delta variant in China have posed a concern about how to properly respond to the battle against evolving COVID-19. Here, we analyzed the "hierarchical and classified prevention and control (HCPC)" measures strategy deployed during the recent Guangzhou outbreak. METHODS: A modified susceptible-exposed-pre-symptomatic-infectious-recovered (SEPIR) model was developed and applied to study a range of different scenarios to evaluate the effectiveness of policy deployment. We simulated severe different scenarios to understand policy implementation and timing of implementation. Two outcomes were measured: magnitude of transmission and duration of transmission. The outcomes of scenario evaluations were presented relative to the reality case (i.e., 368 cases in 34 days) with 95% confidence interval (CI). RESULTS: Based on our simulation, the outbreak would become out of control with 7 million estimated infections under the assumption of the absence of any interventions than the 153 reported cases in reality in Guangzhou. The simulation on delayed implementation of interventions showed that the total case numbers would also increase by 166.67%-813.07% if the interventions were delayed by 3 days or 7 days. CONCLUSIONS: It may be concluded that timely and more precise interventions including mass testing and graded community management are effective measures for Delta variant containment in China.

A Multi-SCALE Community Network-Based SEIQR Model to Evaluate the Dynamic NPIs of COVID-19.

Regarding the problem of epidemic outbreak prevention and control, infectious disease dynamics models cannot support urban managers in reducing urban-scale healthcare costs through community-scale control measures, as they usually have difficulty meeting the requirements for simulation at different scales. In this paper, we propose combining contact networks at different spatial scales to study the COVID-19 outbreak in Shanghai from March to July 2022, calculate the initial Rt through the number of cases at the beginning of the outbreak, and evaluate the effectiveness of dynamic non-pharmaceutical interventions (NPIs) adopted at different time periods in Shanghai using our proposed approach. In particular, our proposed contact network is a three-layer multi-scale network that is used to distinguish social interactions occurring in areas of different sizes, as well as to distinguish between intensive and non-intensive population contacts. This susceptible-exposure-infection-quarantine-recovery (SEIQR) epidemic model constructed based on a multi-scale network can more effectively assess the feasibility of small-scale control measures, such as assessing community quarantine measures and mobility restrictions at different moments and phases of an epidemic. Our experimental results show that this model can meet the simulation needs at different scales, and our further discussion and analysis show that the spread of the epidemic in Shanghai from March to July 2022 can be successfully controlled by implementing a strict long-term dynamic NPI strategy.

The Moral Foundations of Vaccine Passports.

The debate around vaccine passports has been polarising and controversial. Although the measure allows businesses to resume in-person operations and enables transitioning out of lockdown due to the COVID-19 pandemic, some have expressed concerns about liberty violations and discrimination. Understanding the splintered viewpoints can aid businesses in communicating such measures to employees and consumers. We conceptualise the business implementation of vaccine passports as a moral decision rooted in individual values that influence reasoning and emotional reaction. We surveyed support for vaccine passports on a nationally representative sample in the United Kingdom in 2021: April (n = 349), May (n = 328), and July (n = 311). Drawing on the Moral Foundations Theory-binding (loyalty, authority, and sanctity), individualising (fairness and harm), and liberty values-we find that individualising values are a positive predictor and liberty values a negative predictor of support for passports, suggesting adoption hinges on addressing liberty concerns. Longitudinal analysis examining the trajectory of change in support over time finds that individualising foundations positively predict changes in utilitarian and deontological reasoning over time. In contrast, a fall in anger over time predicts increased support towards vaccine passports. Our study can inform business and policy communication strategies of existing vaccine passports, general vaccine mandates, and similar measures in future pandemics.

Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses.

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students' learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility-a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

Combating COVID-19 with charisma: Evidence on governor speeches in the United States.

Using field and laboratory data, we show that leader charisma can affect COVID-related mitigating behaviors. We coded a panel of U.S. governor speeches for charisma signaling using a deep neural network algorithm. The model explains variation in stay-at-home behavior of citizens based on their smart phone data movements, showing a robust effect of charisma signaling: stay-at-home behavior increased irrespective of state-level citizen political ideology or governor party allegiance. Republican governors with a particularly high charisma signaling score impacted the outcome more relative to Democratic governors in comparable conditions. Our results also suggest that one standard deviation higher charisma signaling in governor speeches could potentially have saved 5,350 lives during the study period (02/28/2020-05/14/2020). Next, in an incentivized laboratory experiment we found that politically conservative individuals are particularly prone to believe that their co-citizens will follow governor appeals to distance or stay at home when exposed to a speech that is high in charisma; these beliefs in turn drive their preference to engage in those behaviors. These results suggest that political leaders should consider additional "soft-power" levers like charisma-which can be learned-to complement policy interventions for pandemics or other public heath crises, especially with certain populations who may need a "nudge."

[Reduction of adverse outcome due to COVID-19 infection in a high-risk population: evaluation of an informative intervention through active call by General Practitioners]. / Riduzione degli outcome sfavorevoli dovuti all'infezione da COVID-19 su una popolazione ad alto rischio: valutazione di un intervento informativo mediante chiamata attiva da parte dei medici di medicina generale.

BACKGROUND: currently, individuals at risk of adverse outcomes for COVID-19 can access to vaccination and pharmacological interventions. But, during the first epidemic wave, there were no treatments or therapeutic strategies available to reduce adverse outcomes in patients at risk. OBJECTIVES: to assess the impact of an intervention at 15-month follow-up developed by the Agency for Health Protection of the Metropolitan Area of Milan (ATS Milan) based on telephone triage and consultation by the General Practitioners (GPs) for patient with high-risk for adverse outcomes. DESIGN: intervention on population. SETTING AND PARTICIPANTS: a total of 127,292 patients in the ATS aged ≥70 years and with comorbidities associated with an increased risk of dying from COVID-19 infection were identified. Using a specific information system, patients were assigned to their GPs for telephone triage and consultation. GPs inform them about the risks of the disease, non-pharmacological prevention measures, and precautions in contacts with family members and other persons. No specific clinical intervention was carried out, only an information/training intervention was performed. MAIN OUTCOME MEASURES: by the end of May 2020, 48.613 patients had been contacted and 78.679 had not been contacted. Hazard Ratios (HRs) of infection hospitalisation and death at 3 and 15 months were estimated using Cox regression models adjusted by confounder. RESULTS: no differences in gender, age class distribution, prevalence of specific diseases, and Charlson Index were found between the two groups (treated such as called patients and not called). Called patients had a higher propensity for influenza and antipneumococcal vaccination and have more comorbidities and greater access to pharmacological therapies. Non-called patients have a greater risk for COVID-19 infection: HR was 3.88 (95%CI 3.48-4.33) at 3 months and 1.28 (95%CI 1.23-1.33) at 15 months; for COVID-19 hospitalization HR was 2.66 (95%CI 2.39-2,95) at 3 months and 1.31 (95%CI 1.25-1.37) at 15 months; for overall mortality HR was 2,52 (95%CI 2.35-2:72) at 3 months and 1.23 (95%CI 1.19-1.27) at 15 months. CONCLUSIONS: the results of this study show a reduction in hospitalization and deaths and support, in case of pandemic events, the implementation of new care strategies based on adapted stratification systems in order to protect the population's health. This study presents some limits: it is not randomized; a selection bias is present (called patients were those most in contact with the GPs); the intervention is indication-based (on march 2020, the actual benefit of protection and distancing for high-risk groups was unclear), and the adjustment is not able to fully control for confounding. However, this study points out the importance to develop information systems and improve methods to best protect the health of the population in setting of territorial epidemiology.

Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic.

Introduction: A discussion of 'waves' of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous. Methods: We present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as 'observed waves'. This provides an objective means of describing observed waves in time series. We use this method to synthesize evidence across different countries to study types, drivers and modulators of waves. Results: The output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of NPIs correlates with a reduced number of observed waves and reduced mortality burden in those waves. Conclusion: It is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.

Non-pharmaceutical interventions in the State of Georgia: Economic implications.

Background: As Covid-19 spread rapidly, many countries implemented a strict shelter-in-place to "flatten the curve" and build capacity to treat in the absence of effective preventative therapies or treatments. Policymakers and public health officials must balance the positive health effects of lockdowns with economic, social, and psychological costs. This study examined the economic impacts of state and county level restrictions during the 2020 Covid-19 pandemic for two regions of Georgia. Methods: Taking unemployment data from the Opportunity Insights Economic Tracker with mandate information from various sites, we examined trends before and after a mandate's implementation and relaxation using joinpoint regression. Results: We found mandates with the largest impact on unemployment claims rates were the shelters-in-place (SIPs) and closures of non-essential businesses. Specific to our study, mandates had an effect where first implemented, i.e., if the state implemented an SIP after the county, the state-wide SIP had no additional measurable effect on claims rates. School closures had a consistent impact on increasing unemployment claims rates, but to a lesser degree than SIPs or business closures. While closing businesses did have a deleterious effect, implementing social distancing for businesses and restricting gatherings did not. Notably, the Coastal region was less affected than the Metro Area. Additionally, our findings indicate that race ethnicity may be a larger predictor of adverse economic effects than education, poverty level, or geographic area. Conclusions: Our findings coincided with other studies in some areas but showed differences in what indicators may best predict adverse effects and that coastal communities may not always be as impacted as other regions in a state. Ultimately, the most restrictive measures consistently had the largest negative economic impacts. Social distancing and mask mandates can be effective for containment while mitigating the economic impacts of strict SIPs and business closures.

Changes in awareness on face mask use in Korea.

The present research aims to determine, from the perspective of public health nursing, how Koreans have implemented the mandatory use of face masks during the COVID-19 outbreak by increasing public awareness in the following order: familiarly wearing, frequently wearing, and always wearing a face mask. Other nations may consider applying in their own policies the lessons learned by Korea regarding changes in awareness on face mask use.

Data-Driven Estimation of Effectiveness of COVID-19 Non-pharmaceutical Intervention Policies

Non-pharmaceutical Interventions (NPIs), such as Stay-at-Home, and Face-Mask-Mandate, are essential components of the public health response to contain an outbreak like COVID-19. However, it is very challenging to quantify the individual or joint effectiveness of NPIs and their impact on people from different racial and ethnic groups or communities in general. Therefore, in this paper, we study the following two research questions: 1) How can we quantitatively estimate the effectiveness of different NPI policies pertaining to the COVID-19 pandemic?;and 2) Do these policies have considerably different effects on communities from different races and ethnicity? To answer these questions, we model the impact of an NPI as a joint function of stringency and effectiveness over a duration of time. Consequently, we propose a novel stringency function that can provide an estimate of how strictly an NPI was implemented on a particular day. Next, we applied two popular tree-based discriminative classifiers, considering the change in daily COVID cases and death counts as binary target variables, while using stringency values of different policies as independent features. Finally, we interpreted the learned feature weights as the effectiveness of COVID-19 NPIs. Our experimental results suggest that, at the country level, restaurant closures and stay-at-home policies were most effective in restricting the COVID-19 confirmed cases and death cases respectively;and overall, restaurant closing was most effective in hold-down of COVID-19 cases at individual community levels such as Asian, White, Black, AIAN and, NHPI. Additionally, we also performed a comparative analysis between race-specific effectiveness and country-level effectiveness to see whether different communities were impacted differently. Our findings suggest that the different policies impacted communities (race and ethnicity) differently. © 2022 IEEE.

School's Out? Simulating Schooling Strategies During COVID-19

Multi-agent based systems offer the possibility to examine the effects of policies down to specific target groups while also considering the effects on a population-level scale. To examine the impact of different schooling strategies, an agent-based model is used in the context of the COVID-19 pandemic using a German city as an example. The simulation experiments show that reducing the class size by rotating weekly between in-person classes and online schooling is effective at preventing infections while driving up the detection rate among children through testing during weeks of in-person attendance. While open schools lead to higher infection rates, a surprising result of this study is that school rotation is almost as effective at lowering infections among both the student population and the general population as closing schools. Due to the continued testing of attending students, the overall infections in the general population are even lower in a school rotation scenario, showcasing the potential for emergent behaviors in agent-based models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

IMPACTS OF COVID-19 NON-PHARMACEUTICAL INTERVENTIONS ON TRADE FLOWS: A GLOBAL PANEL VECTOR AUTOREGRESSION ANALYSIS

The COVID-19 outbreak has contributed to a tremendous global decline in international trade flows. The rapid spread of the disease and the control measures implemented by governments to contain the virus have led to serious consequences for the global economy. The pandemic has affected the international movement of people, goods, and services. Currently, the systematic quantitative research investigating the effects of specific non-pharmaceutical intervention policy clusters on country-level international trade flows, remains limited. In this study, the Panel Vector Autoregression (PVAR) method was conducted using country-level panel data collected from various international sources including the United Nations, World Bank, and University of Oxford. The results show that stringent COVID-19 closure, social distancing, and containment measures and health-related measures, had significant negative impacts on trade flows. In contrast, economic support measures showed significant positive effects on trade. In summary, the findings suggest that policymakers should maintain less stringent containment measures related to public closure and movement restrictions and stimulate economic activities through economic support policies in order to minimize losses in trade flows during the pandemic. © 2023,ABAC Journal. All Rights Reserved.

Training in lockdown: The impact of stringency measures on the Tokyo 2020 Olympic Games

Due to COVID-19, the 32nd Olympic Games were postponed temporarily for the first time, apart from those cancelled during the First and Second World Wars. Did the pandemic also affect the results? We aim to understand the impact of stringency measures on athletes' performance in the Olympics. For many athletes, the Olympics are the pinnacle of their careers, and they follow intense training schedules to arrive at the Games in peak physical condition. Stringency measures may have affected their results by making it harder for them to train effectively, to access sports infrastructure, to meet teammates, and more generally to follow an athletic lifestyle. Our quantitative analysis shows that stringency measures had an effect on the number of Olympic medals won, especially in team sports. This is consistent with the idea that stricter non-pharmaceutical interventions made it harder for teams to train together and achieve the necessary chemistry and harmony to succeed in such a competitive event. Furthermore, women were more severely penalized by higher stringency measures than men in team events. © The Author(s) 2023.

Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation.

OBJECTIVE: In 2020, the coronavirus disease 2019 (COVID-19) respiratory infection is spreading in Korea. In order to prevent the spread of an infectious disease, infected people must be quickly identified and isolated, and contact with the infected must be blocked early. This study attempted to verify the intervention effects on the spread of an infectious disease by using these measures in a mathematical model. METHODS: We used the susceptible-infectious-recovery (SIR) model for a virtual population group connected by a special structured network. In the model, the infected state (I) was divided into I in which the infection is undetected and Ix in which the infection is detected. The probability of transitioning from an I state to Ix can be viewed as the rate at which an infected person is found. We assumed that only those connected to each other in the network can cause infection. In addition, this study attempted to evaluate the effects of isolation by temporarily removing the connection among these people. RESULTS: In Scenario 1, only the infected are isolated; in Scenario 2, those who are connected to an infected person and are also found to be infected are isolated as well. In Scenario 3, everyone connected to an infected person are isolated. In Scenario 3, it was possible to effectively suppress the infectious disease even with a relatively slow rate of diagnosis and relatively high infection rate. CONCLUSION: During the epidemic, quick identification of the infected is helpful. In addition, it was possible to quantitatively show through a simulation evaluation that the management of infected individuals as well as those who are connected greatly helped to suppress the spread of infectious diseases.

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study.

BACKGROUND: The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China. METHODS: We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011-2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020-2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020-2021. RESULTS: The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011-2019, with an obvious winter-spring seasonality in Beijing. During the 2020-2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, "Cancelling public events" and "Restrictions on internal travel" measures played an important role in the reduction of ILI in Beijing, while the "restrictions on international travel" was statistically most associated with ILI reductions in the Hong Kong SAR. CONCLUSIONS: Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.

Non-pharmaceutical interventions for COVID-19 reduced the incidence of infectious diseases: a controlled interrupted time-series study.

BACKGROUND: Non-pharmaceutical interventions (NPIs) have been implemented worldwide to suppress the spread of coronavirus disease 2019 (COVID-19). However, few studies have evaluated the effect of NPIs on other infectious diseases and none has assessed the avoided disease burden associated with NPIs. We aimed to assess the effect of NPIs on the incidence of infectious diseases during the COVID-19 pandemic in 2020 and evaluate the health economic benefits related to the reduction in the incidence of infectious diseases. METHODS: Data on 10 notifiable infectious diseases across China during 2010-2020 were extracted from the China Information System for Disease Control and Prevention. A two-stage controlled interrupted time-series design with a quasi-Poisson regression model was used to examine the impact of NPIs on the incidence of infectious diseases. The analysis was first performed at the provincial-level administrative divisions (PLADs) level in China, then the PLAD-specific estimates were pooled using a random-effect meta-analysis. RESULTS: A total of 61,393,737 cases of 10 infectious diseases were identified. The implementation of NPIs was associated with 5.13 million (95% confidence interval [CI] 3.45‒7.42) avoided cases and USD 1.77 billion (95% CI 1.18‒2.57) avoided hospital expenditures in 2020. There were 4.52 million (95% CI 3.00‒6.63) avoided cases for children and adolescents, corresponding to 88.2% of total avoided cases. The top leading cause of avoided burden attributable to NPIs was influenza [avoided percentage (AP): 89.3%; 95% CI 84.5‒92.6]. Socioeconomic status and population density were effect modifiers. CONCLUSIONS: NPIs for COVID-19 could effectively control the prevalence of infectious diseases, with patterns of risk varying by socioeconomic status. These findings have important implications for informing targeted strategies to prevent infectious diseases.

Non-pharmaceutical Interventions and COVID-19: Do County- and State-Level Policies Predict the Spread of COVID-19?

This study examines the impact of county- and state-level policies on the spread and severity of COVID-19 in communities in the USA during the first wave of the COVID-19 pandemic. We use county-level COVID-19 death and case data to examine the impact of county- and state-level mandates and non-pharmaceutical interventions (NPIs) on the spread and severity of COVID-19. Following previous work by Amuendo-Dorantes et al. (2020), we utilize a strategy that incorporates the duration of NPI implementation within a county. Specifically, we examine aggregated measures of mask mandates, daycare closures, stay-at-home orders, and restaurant and bar closures. In addition to the implementation and duration of NPI policy, we examine the role of pre-existing factors that contribute to social determinants of health in a locality. We incorporate information on the incidence of prior health conditions, socio-economic factors, and demographics including racial and ethnic composition, share of immigrant population of counties, and state governance in our estimations. To alleviate the possible endogeneity of COVID-19 outcomes and NPIs, we use instrumental variable estimation and our results show that collectively NPIs decreased the intensity of the pandemic by decreasing the total deaths and cases. Furthermore, we find the magnitude of the impact of NPIs increases the longer they are implemented. We also estimate a specification that allows for heterogeneity of NPI impact based on the racial and ethnic composition of counties. Our results suggest that NPIs have a non-uniform impact in counties with different racial and ethnic compositions.

Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures.

We provided a framework of a mathematical epidemic modeling and a countermeasure against the novel coronavirus disease (COVID-19) under no vaccines and specific medicines. The fact that even asymptomatic cases are infectious plays an important role for disease transmission and control. Some patients recover without developing the disease; therefore, the actual number of infected persons is expected to be greater than the number of confirmed cases of infection. Our study distinguished between cases of confirmed infection and infected persons in public places to investigate the effect of isolation. An epidemic model was established by utilizing a modified extended Susceptible-Exposed-Infectious-Recovered model incorporating three types of infectious and isolated compartments, abbreviated as SEIIIHHHR. Assuming that the intensity of behavioral restrictions can be controlled and be divided into multiple levels, we proposed the feedback controller approach to implement behavioral restrictions based on the active number of hospitalized persons. Numerical simulations were conducted using different detection rates and symptomatic ratios of infected persons. We investigated the appropriate timing for changing the degree of behavioral restrictions and confirmed that early initiating behavioral restrictions is a reasonable measure to reduce the burden on the health care system. We also examined the trade-off between reducing the cumulative number of deaths by the COVID-19 and saving the cost to prevent the spread of the virus. We concluded that a bang-bang control of the behavioral restriction can reduce the socio-economic cost, while a control of the restrictions with multiple levels can reduce the cumulative number of deaths by infection.

Factors associated with non-pharmaceutical interventions compliance during COVID-19 pandemic: a Portuguese cross-sectional survey.

BACKGROUND: The use of non-pharmaceutical interventions (NPI) is one of the main tools used in the coronavirus disease 2019 (COVID-19) pandemic response, including physical distancing, frequent hand washing, face mask use, respiratory hygiene and use of contact tracing apps. Literature on compliance with NPI during the COVID-19 pandemic is limited. METHODS: We studied this compliance and associated factors in Portugal, between 28th October 2020 and 11th January 2021 (Portuguese second and third waves of the pandemic), using logistic regressions. Data were collected through a web-based survey and included questions regarding NPI compliance, COVID-19-related concerns, perception of institutions' performance, agreement with the measures implemented and socio-demographic characteristics. RESULTS: From the 1263 eligible responses, we found high levels of compliance among all COVID-19 related NPI, except for the contact tracing app. Females and older participants showed the highest compliance levels, whereas the opposite was observed for previously infected participants. There was heterogeneity of COVID-19 NPI compliance across Portuguese regions and a clear gradient between concern, government performance's perception or agreement and compliance. CONCLUSIONS: Results suggested areas for further study with important implications for pandemic management and communication, for future preparedness, highlighting other factors to be accounted for when recommending policy measures during public health threats.