Unintended consequences of implementing non-pharmaceutical interventions for the COVID-19 response in Africa: experiences from DRC, Nigeria, Senegal, and Uganda.

INTRODUCTION: The coronavirus (COVID 19) pandemic is one of the most terrifying disasters of the twenty-first century. The non-pharmaceutical interventions (NPIs) implemented to control the spread of the disease had numerous positive consequences. However, there were also unintended consequences-positively or negatively related to the nature of the interventions, the target, the level and duration of implementation. This article describes the unintended economic, Psychosocial and environmental consequences of NPIs in four African countries. METHODS: We conducted a mixed-methods study in the Democratic Republic of Congo (DRC), Nigeria, Senegal and Uganda. A comprehensive conceptual framework, supported by a clear theory of change was adopted to encompass both systemic and non-systemic interventions. The data collection approaches included: (i) review of literature; (ii) analysis of secondary data for selected indicators; and (ii) key informant interviews with policy makers, civil society, local leaders, and law enforcement staff. The results were synthesized around thematic areas. RESULTS: Over the first six to nine months of the pandemic, NPIs especially lockdowns, travel restrictions, curfews, school closures, and prohibition of mass gathering resulted into both positive and negative unintended consequences cutting across economic, psychological, and environmental platforms. DRC, Nigeria, and Uganda observed reduced crime rates and road traffic accidents, while Uganda also reported reduced air pollution. In addition, hygiene practices have improved through health promotion measures that have been promoted for the response to the pandemic. All countries experienced economic slowdown, job losses heavily impacting women and poor households, increased sexual and gender-based violence, teenage pregnancies, and early marriages, increased poor mental health conditions, increased waste generation with poor disposal, among others. CONCLUSION: Despite achieving pandemic control, the stringent NPIs had several negative and few positive unintended consequences. Governments need to balance the negative and positive consequences of NPIs by anticipating and instituting measures that will support and protect vulnerable groups especially the poor, the elderly, women, and children. Noticeable efforts, including measures to avoid forced into marriage, increasing inequities, economic support to urban poor; those living with disabilities, migrant workers, and refugees, had been conducted to mitigate the negative effects of the NIPs.

Impact of tiered measures on social contact and mixing patterns of in Italy during the second wave of COVID-19.

BACKGROUND: Most countries around the world enforced non-pharmaceutical interventions against COVID-19. Italy was one of the first countries to be affected by the pandemic, imposing a hard lockdown, in the first epidemic wave. During the second wave, the country implemented progressively restrictive tiers at the regional level according to weekly epidemiological risk assessments. This paper quantifies the impact of these restrictions on contacts and on the reproduction number. METHODS: Representative (with respect to age, sex, and region of residence) longitudinal surveys of the Italian population were undertaken during the second epidemic wave. Epidemiologically relevant contact patterns were measured and compared with pre-pandemic levels and according to the level of interventions experienced by the participants. Contact matrices were used to quantify the reduction in the number of contacts by age group and contact setting. The reproduction number was estimated to evaluate the impact of restrictions on the spread of COVID-19. RESULTS: The comparison with the pre-pandemic baseline shows a significant decrease in the number of contacts, independently from the age group or contact settings. This decrease in the number of contacts significantly depends on the strictness of the non-pharmaceutical interventions. For all levels of strictness considered, the reduction in social mixing results in a reproduction number smaller than one. In particular, the impact of the restriction on the number of contacts decreases with the severity of the interventions. CONCLUSIONS: The progressive restriction tiers implemented in Italy reduced the reproduction number, with stricter interventions associated with higher reductions. Readily collected contact data can inform the implementation of mitigation measures at the national level in epidemic emergencies to come.

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."

Lessons drawn from Shanghai for controlling highly transmissible SARS-CoV-2 variants: insights from a modelling study.

BACKGROUND: The continuous emergence of novel SARS-CoV-2 variants with markedly increased transmissibility presents major challenges to the zero-COVID policy in China. It is critical to adjust aspects of the policy about non-pharmaceutical interventions (NPIs) by searching for and implementing more effective ways. We use a mathematical model to mimic the epidemic pattern of the Omicron variant in Shanghai to quantitatively show the control challenges and investigate the feasibility of different control patterns in avoiding other epidemic waves. METHODS: We initially construct a dynamic model with a core step-by-step release strategy to reveal its role in controlling the spread of COVID-19, including the city-based pattern and the district-based pattern. We used the least squares method and real reported case data to fit the model for Shanghai and its 16 districts, respectively. Optimal control theory was utilized to explore the quantitative and optimal solutions of the time-varying control strength (i.e., contact rate) to suppress the highly transmissible SARS-CoV-2 variants. RESULTS: The necessary period for reaching the zero-COVID goal can be nearly 4 months, and the final epidemic size was 629,625 (95%CI: [608,049, 651,201]). By adopting the city-based pattern, 7 out of 16 strategies released the NPIs more or earlier than the baseline and ensured a zero-resurgence risk at the average cost of 10 to 129 more cases in June. By adopting the district-based pattern, a regional linked release can allow resumption of social activity to ~ 100% in the boundary-region group about 14 days earlier and allow people to flow between different districts without causing infection resurgence. Optimal solutions of the contact rate were obtained with various testing intensities, and higher diagnosis rate correlated with higher optimal contact rate while the number of daily reported cases remained almost unchanged. CONCLUSIONS: Shanghai could have been bolder and more flexible in unleashing social activity than they did. The boundary-region group should be relaxed earlier and more attention should be paid to the centre-region group. With a more intensive testing strategy, people could return to normal life as much as possible but still ensure the epidemic was maintained at a relatively low level.

Disentangling the effect of measures, variants, and vaccines on SARS-CoV-2 infections in England: A dynamic intensity model

In this paper, we estimate the path of daily SARS-CoV-2 infections in England from the beginning of the pandemic until the end of 2021. We employ a dynamic intensity model, where the mean intensity conditional on the past depends both on past intensity of infections and past realized infections. The model parameters are time-varying, and we employ a multiplicative specification along with logistic transition functions to disentangle the time-varying effects of nonpharmaceutical policy interventions, of different variants, and of protection (waning) of vaccines/boosters. Our model results indicate that earlier interventions and vaccinations are key to containing an infection wave. We consider several scenarios that account for more infectious variants and different protection levels of vaccines/boosters. These scenarios suggest that, as vaccine protection wanes, containing a new wave in infections and an associated increase in hospitalizations in the near future may require further booster campaigns and/or nonpharmaceutical interventions.

COVID‐19 Policy Interventions and Fertility Dynamics in the Context of Pre‐Pandemic Welfare Support

This paper focuses on nonpharmaceutical interventions (NPIs) to explain fertility dynamics during the pandemic, while considering countries' institutional context. We argue that containment policies disrupted people's lives and increased their uncertainty more in countries with weak welfare support systems, while health‐related and economic support NPIs mitigated such disruptions much more there, as they were less expected by citizens. We estimate monthly "excess” crude birth rates (CBRs) and find that countries with low public support—Southern Europe, East Asia, and Eastern Europe—experienced larger decreases and less of a rebound in CBRs than countries with histories of high public spending—Western, Central, and Northern Europe. However, in low support countries, NPIs are much more strongly associated with excess CBRs—containment NPIs more negatively and health and economic support NPIs more positively—with the exception of the one‐month lag of containment NPIs, for which the opposite holds. When putting these coefficients into broader perspective, our findings suggest that the actual implementation of all NPIs taken together mitigated fertility declines. This is especially the case for low public support countries, whereas one might have seen a birth decline even in high support countries if the NPIs were not implemented. [ FROM AUTHOR] Copyright of Population & Development Review is the property of Wiley-Blackwell 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.)

Global Trends in Social Prescribing: Web-Based Crawling Approach.

BACKGROUND: Social loneliness is a prevalent issue in industrialized countries that can lead to adverse health outcomes, including a 26% increased risk of premature mortality, coronary heart disease, stroke, depression, cognitive impairment, and Alzheimer disease. The United Kingdom has implemented a strategy to address loneliness, including social prescribing-a health care model where physicians prescribe nonpharmacological interventions to tackle social loneliness. However, there is a need for evidence-based plans for global social prescribing dissemination. OBJECTIVE: This study aims to identify global trends in social prescribing from 2018. To this end, we intend to collect and analyze words related to social prescribing worldwide and evaluate various trends of related words by classifying the core areas of social prescribing. METHODS: Google's searchable data were collected to analyze web-based data related to social prescribing. With the help of web crawling, 3796 news items were collected for the 5-year period from 2018 to 2022. Key topics were selected to identify keywords for each major topic related to social prescribing. The topics were grouped into 4 categories, namely Healthy, Program, Governance, and Target, and keywords for each topic were selected thereafter. Text mining was used to determine the importance of words collected from new data. RESULTS: Word clouds were generated for words related to social prescribing, which collected 3796 words from Google News databases, including 128 in 2018, 432 in 2019, 566 in 2020, 748 in 2021, and 1922 in 2022, increasing nearly 15-fold between 2018 and 2022 (5 years). Words such as health, prescribing, and GPs (general practitioners) were the highest in terms of frequency in the list for all the years. Between 2020 and 2021, COVID, gardening, and UK were found to be highly related words. In 2022, NHS (National Health Service) and UK ranked high. This dissertation examines social prescribing-related term frequency and classification (2018-2022) in Healthy, Program, Governance, and Target categories. Key findings include increased "Healthy" terms from 2020, "gardening" prominence in "Program," "community" growth across categories, and "Target" term spikes in 2021. CONCLUSIONS: This study's discussion highlights four key aspects: (1) the "Healthy" category trends emphasize mental health, cancer, and sleep; (2) the "Program" category prioritizes gardening, community, home-schooling, and digital initiatives; (3) "Governance" underscores the significance of community resources in social prescribing implementation; and (4) "Target" focuses on 4 main groups: individuals with long-term conditions, low-level mental health issues, social isolation, or complex social needs impacting well-being. Social prescribing is gaining global acceptance and is becoming a global national policy, as the world is witnessing a sharp rise in the aging population, noncontagious diseases, and mental health problems. A successful and sustainable model of social prescribing can be achieved by introducing social prescribing schemes based on the understanding of roles and the impact of multisectoral partnerships.

The effect of self-esteem on the spread of a pandemic. A cross-country analysis of the role played by self-esteem in the spread of the COVID-19 pandemic.

Extant research on COVID-19 suggests that many socio-economic determinants, by affecting personal behavior, have influenced the evolution of the pandemic. In this paper we study the role played in this regard by average levels of self-esteem in the public. There are reasons to believe that both low and very levels of self-esteem may have an effect on the spread of COVID-19, for opposite reasons. On the one hand, people with low self-esteem may not worry enough to behave in the way recommended (and prescribed, through non-pharmaceutical interventions) by the authorities; people with very high self-esteem, on the other hand, may be over-confident and fail to follow the prescriptions, believing that they do not need them. In this study we test this hypothesis by means of a quantitative cross-country analysis, using a hybrid model and the Rosenberg self-esteem scale. Our results suggest the existence of a U-shaped relationship between the trend of COVID-19 and average levels of self-esteem in a country.

Inferring UK COVID-19 fatal infection trajectories from daily mortality data: Were infections already in decline before the UK lockdowns?

The number of new infections per day is a key quantity for effective epidemic management. It can be estimated relatively directly by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen-effective reproduction number, R, using data gathered from the clinical response to the disease. For coronavirus disease 2019 (Covid-19/SARS-Cov-2), such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty, it is useful to retrospectively reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on first-wave Covid-19 deaths and the disease duration distribution suggests that fatal infections were in decline before full UK lockdown (24 March 2020), and that fatal infections in Sweden started to decline only a day or two later. An analysis of UK data using the model of Flaxman et al. gives the same result under relaxation of its prior assumptions on R, suggesting an enhanced role for non-pharmaceutical interventions short of full lockdown in the UK context. Similar patterns appear to have occurred in the subsequent two lockdowns.

Impact of COVID-19 on travel intention in Silicon Valley of India - testing the efficacy of the theory of planned behaviour

Travel intention is an important factor in understanding the traveller's preferences in the travel and tourism business. The purpose of the research is to understand the impact of COVID-19 on travel intention in the Silicon Valley of India and to test the efficacy of the theory of planned behaviour to predict travel intentions. This empirical research followed a quantitative analysis using the data acquired from a structured questionnaire. Convenience sampling was followed to collect the data from the respondents. The multiple linear regression results indicate a significant relationship between attitude, subjective norm, and perceived behaviour control on the intention to travel. The research is limited to respondents from Silicon Valley. The study contributes to the limited literature available in developing countries with respect to understanding the efficacy of the theory of planned behaviour to predict travel intention. The policymakers and health authorities can use the study findings to make decisions and develop policy regulations during the pandemic. Copyright © 2023 Inderscience Enterprises Ltd.

Support for those not affected: How macroeconomic policies have shaped COVID's impact on sectoral activity

Considering the different dynamics of the COVID-triggered real shocks and macroeconomic measures during the first two waves of the pandemic, using official quarterly data for 2020 the paper studies how economic support measures have impacted the economic performance of sectors in EU countries. The paper shows that: (1) for all analysed sectoral groups, the economic support measures effects were much smaller than the corresponding non-pharmaceutical interventions impacts;(2) sectoral groups did not receive economic support in proportion to the corresponding nonpharmaceutical interventions;(3) sectoral groups with greater non-pharmaceutical interventions did not receive even systematically bigger effects of the economic support measures;and (4) the net overall effects of the COVID-triggered measures on sectoral value added vary considerably between sectors. By evaluating the potential opportunity costs of the ‘flat' economic support activity, the study's results also help to identify measures that could eventually be implemented once the pandemic has ended to mitigate the potential opportunity costs of the sector non-specific implementation of economic support measures, which will become apparent in the recovery period following the pandemic. © 2022 Economic Society of Australia, Queensland

Bayesian time-varying autoregressive models of COVID-19 epidemics.

The COVID-19 pandemic has highlighted the importance of reliable statistical models which, based on the available data, can provide accurate forecasts and impact analysis of alternative policy measures. Here we propose Bayesian time-dependent Poisson autoregressive models that include time-varying coefficients to estimate the effect of policy covariates on disease counts. The model is applied to the observed series of new positive cases in Italy and in the United States. The results suggest that our proposed models are capable of capturing nonlinear growth of disease counts. We also find that policy measures and, in particular, closure policies and the distribution of vaccines, lead to a significant reduction in disease counts in both countries.

Impact of combination preventative interventions on hospitalization and death under the pandemic of SARS-CoV-2 Omicron variant in China.

With a large population most susceptible to Omicron and emerging SARS-CoV-2 variants, China faces uncertain scenarios if reopening its border. Thus, we aimed to predict the impact of combination preventative interventions on hospitalization and death. An age-stratified susceptible-infectious-quarantined-hospitalized-removed-susceptible (SIQHRS) model based on the new guidelines of COVID-19 diagnosis and treatment (the ninth edition) was constructed to simulate the transmission dynamics of Omicron within 365 days. At baseline, we assumed no interventions other than 60% booster vaccination in individuals aged <=60 years and 80% in individuals aged >60 years, quarantine and hospitalization. Oral antiviral medications for COVID-19 (e.g. BRII-196/BRII-198) and non-pharmaceutical interventions (NPIs) such as social distancing and antigen self-testing were considered in subsequent scenarios. Sensitivity analyses were conducted to reflect different levels of interventions. A total of 0.73 billion cumulative quarantines (95% CI 0.53-0.83), 33.59 million hospitalizations (22.41-39.31), and 0.62 million deaths (0.40-0.75) are expected in 365 days. The case fatality rate with pneumonia symptoms (moderate, severe and critical illness) is expected to be 1.83% (1.68-1.99%) and the infected fatality rate 0.38‰ (0.33-0.42‰). The highest existing hospitalization and ICU occupations are 3.11 (0.30-3.85) and 20.33 (2.01-25.20) times of capacity, respectively. Sensitivity analysis showed that interventions can be adjusted to meet certain conditions to reduce the total number of infections and deaths. In conclusion, after sufficient respiratory and ICU beds are prepared and the relaxed NPIs are in place, the SARS-CoV-2 Omicron variant would not seriously impact the health system. This article is protected by copyright. All rights reserved.

Effects of contact tracing and nucleic acid testing on the COVID-19 outbreak in Zunyi, China: data-driven study using a branching process model.

BACKGROUND: During October 2021, China experienced localized outbreaks of COVID-19 in many cities. We analyzed the small local outbreak in Zunyi (Guizhou Province), a major city in southwestern China, and modeled the effects of different interventions on this outbreak. METHODS: Data on infections and contacts, provided by the Health Commission of Guizhou Province, were used to analyze the epidemiological characteristics of the outbreak and calculate the effectiveness of vaccination. A branching process model was used to simulate the outbreak. This model considered the time interval from exposure of the initial case to confirmation, the number of potential infections caused by the initial case, and the effects of the different interventions. RESULTS: From 18 to 25 October 2021, there were 12 patients with COVID-19 in Zunyi. Overall, the average age was 67.17 years-old, 8 patients were females, and 1 patient had an asymptomatic infection. The effectiveness of two-dose inactivated vaccine against SARS-CoV-2 infection was 16.7% (95% CI: 2.8% to 99.7%). The initial case was infected on 11 or 12 October 2021, 6.40 (95% CI: 6.37, 6.42; IQR: 4.92, 7.63) days before confirmation while the travelling in Lanzhou (Gansu Province). There were 10.07 (95% CI: 10.04, 10.09; IQR: 7.86, 11.93) potential secondary cases. When the effective vaccine coverage reached 60%, the probability of cumulative cases exceeding 20 was less than 8.77%, even if contact tracing was relaxed or eliminated. However, if the probability of tracing contacts decreased, earlier initiation of nucleic acid testing was necessary to control the outbreak. CONCLUSIONS: The COVID-19 outbreak in Zunyi was controlled quickly due to moderately effective vaccine coverage and rapid contact tracing. For controlling localized outbreaks, vaccination and contact tracing seemed to be more effective than massive nucleic acid testing in the initial phase of transmission. However, if there is low effective vaccine coverage or insufficient contact tracing, nucleic acid testing should start earlier.

Vaccination willingness among undergraduates: Role of conspiracy mentality and belief in Covid-19 vaccine conspiracies.

OBJECTIVE: To investigate willingness to vaccination, conspiracy mentality, and belief in vaccine conspiracies among undergraduate students as well as the level of adherence to non-pharmaceutical interventions during the coronavirus disease-2019 pandemic. METHODS: The cross-sectional study was conducted from January to June, 2021, and comprised undergraduate students from Islamabad and Rawalpindi, Pakistan. Data was gathered using the General Conspiracy Mentality Scale and the Belief in Vaccine Conspiracies Scale. Willingness for vaccination and degree of adherence to non-pharmaceutical interventions was measured on a 5-point rating scale. Data was analysed using SPSS 26. RESULTS: Of the 300 subjects, 154 were males and 146 were females. The overall mean age of the sample was (23.47 ±2.17). A sample of 121(40.33%) respondents believed in vaccine conspiracies, while only 83(27.66%) showed disagreement. High scores on conspiracy mentality (p<0.020) and belief in vaccine conspiracies (p<0.006) were associated with little adherence to behavioural recommendations for coronavirus disease-2019. High scorers on conspiracy mentality (p<0.006) and belief in vaccine conspiracies (p<0.004) had less willingness for vaccination. There was no significant difference in the conspiracy mentality and belief in vaccine conspiracies with reference to gender (p>0.05). CONCLUSIONS: Medical practitioners and healthcare organisations need to understand the connection between belief in vaccine conspiracies and related vaccine resistance and noncompliance with behavioural recommendations in the face of a pandemic.

Support for those not affected: How macroeconomic policies have shaped COVID’s impact on sectoral activity

Considering the different dynamics of the COVID-triggered real shocks and macroeconomic measures during the first two waves of the pandemic, using official quarterly data for 2020 the paper studies how economic support measures have impacted the economic performance of sectors in EU countries. The paper shows that: (1) for all analysed sectoral groups, the economic support measures effects were much smaller than the corresponding non-pharmaceutical interventions impacts;(2) sectoral groups did not receive economic support in proportion to the corresponding nonpharmaceutical interventions;(3) sectoral groups with greater non-pharmaceutical interventions did not receive even systematically bigger effects of the economic support measures;and (4) the net overall effects of the COVID-triggered measures on sectoral value added vary considerably between sectors. By evaluating the potential opportunity costs of the ‘flat’ economic support activity, the study’s results also help to identify measures that could eventually be implemented once the pandemic has ended to mitigate the potential opportunity costs of the sector non-specific implementation of economic support measures, which will become apparent in the recovery period following the pandemic.

Modeling the Impact of Social Determinants of Health on COVID Behaviors in Older Adults using the All of Us Dataset

Non-pharmaceutical interventions such as hand-washing hygiene, avoiding large gatherings, and avoiding visiting nursing homes remain important in mitigating risks of COVID infection among at-risk populations such as older adults. The NIH's All of Us Research Program offers a unique dataset which contains detailed survey data that medical records often lack. Leveraging this dataset and impact scores, we were able to compare deep neural network (DNN) models to more conventional logistic regression and XGBoost models in the task of examining the relationships between social determinants of health and COVID-related behaviors in older adults. LR and DNN models found that African American participants were more likely than White participants to report adherence to guidelines regarding attending large social gatherings, abiding by stay-at-home recommendations and practicing pandemic-related hygiene. Both models also showed that respondents who were employed were less likely than their unemployed/retired counterparts to avoid large social gatherings or participate in activities outside their homes but were more likely to report practice pandemic-related hygiene. DNN models combined with impact scores to explain their output present an alternate approach to modeling outcomes in large, multi-variate cohorts which can outperform conventional statistical modeling. © 2022 IEEE.

Methodology to Compare Twitter Reaction Trends between Disinformation Communities, to COVID related Campaign Events at Different Geospatial Granularities

With still ongoing COVID pandemic, there is an immediate need for a deeper understanding of how Twitter discussions (or chatters) in disinformation spreading communities get triggered. More specifically, the value is in monitoring how such trigger events in Twitter discussion do align with the timelines of relevant influencing events in the society (indicated in this work as campaign events). For campaign events in regards to COVID pandemic, we consider both NPI (Nonpharmaceutical Interventions) campaigns and disinformation spreading campaigns together. In this short paper we have presented a novel methodology to quantify, compare and relate two Twitter disinformation communities, in terms of their reaction patterns to the timelines of major campaign events. We have also analyzed these campaigns at their three geospatial granularity contexts: local county, state, and country/ federal. We have conducted a novel dataset collection on campaigns (NPI + Disinformation) at these different geospatial granularities. Then, with collected dataset on Twitter disinformation communities, we have performed a case study to validate our proposed methodology. © 2022 Public Domain.

Viral infections in hospitalized children in Germany during the COVID-19 pandemic: Association with non-pharmaceutical interventions.

Background: Non-pharmaceutical interventions (NPI) during the COVID-19 pandemic aimed at prevention of SARS-CoV-2 transmission also influenced transmission of viruses other than SARS-CoV-2. The aim of this study was to describe and compare the burden of common viral respiratory and gastrointestinal infections in children admitted to Berlin University Children's Hospital (BCH) before and during the COVID-19 pandemic at different levels of public NPI measures. Methods: In this retrospective study, we analyzed the frequency of detection of common human respiratory and gastrointestinal viruses from January 2016 through January 2022 in all patients admitted to BCH. We compared virus detection before and during the COVID-19 pandemic at different levels of public NPI measures. Results: The frequency of detection of seasonal enveloped and non-enveloped viruses [Boca-, Corona-, Influenza-, Metapneumo-, Parainfluenza-, Rota-, and Respiratory Syncytial Viruses (RSV)] was diminished during the COVID-19 pandemic, whereas detection rates of non-seasonal viruses (Rhino-/Entero-, and Adenoviruses) were stable during the pandemic. After withdrawal of major NPI measures, we observed an out of season surge of the detection rates of Boca-, Corona-, Parainfluenzaviruses, and RSV. In contrast, no increased detection frequency was observed for Influenza-, Metapneumo-, and Rotaviruses as of January 2022. Conclusion: Corona-, Boca-, Parainfluenzaviruses, and RSV returned as frequently detected pathogens after withdrawal of major NPI measures. The out of season rise might be attributed to an "immune-debt" due to missing contact to viral antigens resulting in waning of population immunity during the COVID-19 pandemic.

The role of user centric measures in the use of non-pharmaceutical interventions (NPIs)

Purpose Health authorities have introduced non-pharmaceutical interventions (NPIs) with the aim of reducing the spread of viruses. Against the backdrop of social marketing, normative and utility theories, the purpose of the paper is to examine the relationships between user centric measures such as perceived effectiveness, user satisfaction, and value for effort on intentions to continue to use NPIs. Furthermore, the moderating role of value for effort on user satisfaction and, subsequently, intentions to continue to use NPIs was also considered. Design/methodology/approach A cross-sectional online survey was completed in British Columbia, Canada (N = 287). Analysis was done with partial least squares structural equation modeling. Findings The results show that the relationships between user centric measures are positive and significant on intentions to continue to use NPIs. Furthermore, value for effort moderated the relationship between user satisfaction and intentions to continue to use NPIs - but the relationship was negative. Thus, the higher values of the value for effort construct cause the relationship between user satisfaction and reuse intention to somewhat diminish. Originality/value The results confirm the positive and significant relationships between user centric measures in the context of the use of NPIs and introduce a new understanding of the effect of value for effort on the relationship between user satisfaction and intentions to use NPIs. This enables health officials to better understand how to encourage the use of NPIs.