Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China

Compliance with COVID-19 Physical Distancing Mandates in Oman: The Role of Health Literacy and Internal Health Locus of Control

BACKGROUND

Research indicates that the effectiveness of coronavirus disease 2019 (COVID-19) physical distancing mandates is influenced by several individual factors, including health literacy; internal health locus of control (IHLOC), the belief that physical distancing can reduce COVID-19 risk; social norms; self-efficacy; and perceptions of the benefits and barriers associated with distancing. However, further investigation is needed to understand the links between these factors and compliance intentions.

OBJECTIVE

This study investigates the mechanism linking these factors with the intentions to comply with physical distancing mandates.

METHODS

A total of 759 participants (Mean age = 29.13, standard deviation [SD] = 8.33; 68.5% women) were surveyed online from September 2020 to October 2020. Data were analyzed using ANOVA (analysis of variance) and structural equation modeling.

KEY RESULTS

Health literacy was associated with more perceived benefits (β = .175, p = .001), greater self-efficacy (β = .193, p < .001), and less perceived barriers (β = -.391, p < .001). IHLOC was significantly associated with greater perceived benefits (β = .156, p = .007) and self-efficacy (β = .294, p < .001). Family descriptive norms were significantly associated with fewer perceived barriers (β = -.276, p < .001), while injunctive norms were associated with more perceived benefits (β = .202, p = .001) and higher self-efficacy (β = .299, p < .001). Intentions to adhere to physical distancing mandates were significantly associated with past compliance (β = .427, p < .001) and perceived barriers (β = -.205, p < .001) and benefits (β = .295, p < .001). Post-hoc mediation analyses revealed several small yet significant indirect effects, highlighting the complex pathways shaping adherence intentions.

CONCLUSIONS

This study identifies how health literacy, IHLOC, social norms, perceived benefits and barriers, and self-efficacy intricately shape intentions to comply with physical distancing mandates. These findings offer valuable implications for public health policy and interventions. [HLRP: Health Literacy Research and Practice. 2024;8(2):e69-e78.].

Maritime transportation and people mobility in the early diffusion of COVID-19 in Croatia

Introduction

The outbreak of COVID-19 in Europe began in early 2020, leading to the emergence of several waves of infection with varying timings across European countries. The largest wave of infection occurred in August-September. Croatia, known for being a hotspot of tourism in the Mediterranean region, raised concerns that it might have played a role in incubating the pandemic during the summer of 2020.

Methods

To investigate this possibility, we conducted a data-driven study to examine the potential influence of passenger mobility to and within Croatia, utilizing various modes of transportation. To achieve this, we integrated observational datasets into the "epidemic Renormalization Group" modeling framework.

Results

By comparing the models with epidemiological data, we found that in the case of Croatia in 2020, neither maritime nor train transportation played a prominent role in propagating the infection. Instead, our analysis highlighted the leading role of both road and airborne mobility in the transmission of the virus.

Discussion

The proposed framework serves to test hypotheses concerning the causation of infectious waves, offering the capacity to rule out unrelated factors from consideration.

Direct modelling from GPS data reveals daily-activity-dependency of effective reproduction number in COVID-19 pandemic

During the COVID-19 pandemic, governments faced difficulties in implementing mobility restriction measures, as no clear quantitative relationship between human mobility and infection spread in large cities is known. We developed a model that enables quantitative estimations of the infection risk for individual places and activities by using smartphone GPS data for the Tokyo metropolitan area. The effective reproduction number is directly calculated from the number of infectious social contacts defined by the square of the population density at each location. The difference in the infection rate of daily activities is considered, where the 'stay-out' activity, staying at someplace neither home nor workplace, is more than 28 times larger than other activities. Also, the contribution to the infection strongly depends on location. We imply that the effective reproduction number is sufficiently suppressed if the highest-risk locations or activities are restricted. We also discuss the effects of the Delta variant and vaccination.

Changes in the medical-seeking pattern and daily behavior of hematopoietic stem-cell transplant recipients during the COVID-19 epidemic: An online survey in Hubei Province, China

Background

To curb the spread of the coronavirus disease 2019 (COVID-19) epidemic, the Chinese government shut down Wuhan city from January 23rd to April 8th, 2020. The COVID-19 epidemic not only leads to widespread illness but also affects the diagnosis and treatment of hematopoietic stem-cell transplant (HSCT) recipients.

Objective

To investigate the medical-seeking pattern and daily behavior changes in Hubei Province during the COVID-19 epidemic in Hubei Province during the lockdown.

Methods

We conducted a multicenter, cross-sectional, web-based investigation among 325 HSCT recipients by online questionnaires in Hubei Province during the COVID-19 epidemic.

Results

A total of 145 complete responses were collected both before and during the epidemic questionnaires. The participants from pre-epidemic group preferred to go to hospital (68.29%) when they experienced influenza-like symptoms. The majority of the patients elected to take oral drugs by themselves (40%) or consulted their attending physicians online or by telephone during the lockdown (23.33%). 64.83% had difficulties in purchasing drugs during the lockdown, which was significantly higher than the proportion of the pre-epidemic group (24.83%) (P < 0.05). The participants preferred to purchase drugs online (23.40%) and decrease or withdraw drugs (18.09%) during the epidemic. The number of participants received regular re-examinations during the epidemic decreased sharply. The proportion of wearing masks and isolating themselves at home increased significantly during the epidemic. No statistic difference was observed in the incidence of graft-versus-host disease (GVHD)complications in participants between the during the epidemic group and the pre-epidemic group. In our study, six patients were confirmed to have COVID-19, and half of them died due to COVID-19-related complications.

Conclusion

The medical-seeking pattern and daily behavior of HSCT recipients changed during the lockdown; the methods of self-protection, online consultation and drug delivery can help patients receive necessary follow-up and reduce the occurrence of COVID-19.

A Perspective on the Influence of National Corporate Governance Institutions and Government's Political Ideology on the Speed to Lockdown as a Means of Protection Against Covid-19

This first wave study of the Covid-19 pandemic investigates why the governments of different countries proceeded to lockdown at different speeds. We draw upon the literature on Corporate Governance Institutions (CGIs) to theorize that governments' decision-making is undertaken in the light of prevailing beliefs, norms, and rules of the collectivity, as portrayed by the focal country's CGIs, in their effort to maintain legitimacy. In addition, drawing on motivated cognition we posit that the government's political ideology moderates this relationship because decision-makers are biased when assessing the impact of lockdown on commerce. Running negative binomial regressions on a sample of 125 countries, we find that the more shareholder-oriented the CGIs, the slower the governmental response in shutting down the economy to protect from the pandemic. Moreover, the main relationship is stronger the more right-leaning the government's ideology. Our study contributes to the research on corporate governance institutions and political ideology and illustrates how societal and ideological biases affect government decision-making, especially when important decisions about public welfare are taken with little information on hand.

Back to school or … back to lockdown? The effects of opening schools on the diffusion of COVID-19 in Italian regions

The opening of schools that coincided with the beginning of fall 2020 and the arrival of the second wave of COVID-19 in continental Europe has fostered significant debate in several countries. Some contributions have suggested that youngsters play a minor role in the spread of the virus, given the specific characteristics of this infection; other scholars have raised concerns about the necessary movement that involves keeping schools open, and the consequent potential spread of the virus. In this study, we focus on the Italian case, an interesting setting in which to test the impact of opening schools on the spread of COVID-19, because of the different dates at which schools have opened in the various Italian provinces, and because of the different rates at which the virus has spread across Italy. Our results suggest that open schools have a positive impact on COVID-19 cases, whose spread occurs between 10 and 14 days after opening. While closing schools or using distance learning have other social and economic consequences, making it necessary for policymakers to adopt a holistic evaluation, it should be taken into account that open schools have an impact on the spread of the pandemic.

Analysis of the impact of non-compulsory measures on human mobility in Japan during the COVID-19 pandemic

To curb the spread of the COVID-19 pandemic, countries around the world have imposed restrictions on their population. This study quantitatively assessed the impact of non-compulsory measures on human mobility in Japan during the COVID-19 pandemic, through the analysis of large-scale anonymized mobile-phone data. The non-negative matrix factorization (NMF) method was used to analyze mobile statistics data from the Tokyo area. The results confirmed the suitability of the NMF method for extracting behavior patterns from aggregated mobile statistics data. Data analysis results indicated that although non-pharmaceutical interventions (NPIs) measures adopted by the Japanese government are non-compulsory and rely largely on requests for voluntary self-restriction, they are effective in reducing population mobility and motivating people to practice social distancing. In addition, the current study compared the mobility change in three cities (i.e., Tokyo, Osaka, and Hiroshima), and discussed their similarity and difference in behavior pattern changes during the pandemic. It is expected that the analytical tool proposed in this study can be used to monitor mobility changes in real-time during the pandemic, as well as the long-term evolution of population mobility patterns in the post-pandemic phase.

Experiences of and response to the COVID-19 pandemic at private retail pharmacies in Kenya: a mixed-methods study

OBJECTIVES

To assess experiences of and response to the COVID-19 pandemic at community pharmacies in Kenya.

DESIGN, SETTING AND PARTICIPANTS

This was a mixed-methods study conducted from November 2020 to April 2021, targeting service providers in three counties (Nairobi, Mombasa and Kisumu), selected purposively to represent the main urban centres; pharmacies were selected randomly from a list of licensed pharmacies.

RESULTS

Of 195 sampled pharmacies, 108 (55%) completed a questionnaire and 103 (53%) received a simulated client call; 18 service providers were interviewed. The initial weeks of the pandemic were characterised by fear and panic among service providers and a surge in client flow. Subsequently, 65 (60%) of 108 pharmacies experienced a dip in demand to below prepandemic levels and 34 (31%) reported challenges with unavailability, high price and poor quality of products. Almost all pharmacies were actively providing preventive materials and therapies; educating clients on prevention measures; counselling anxious clients; and handling and referring suspect cases. Fifty-nine pharmacies (55% (95% CI 45% to 65%)) reported receiving a client asking for COVID-19 testing and a similar proportion stated they would support pharmacy-based testing if implemented. For treatment of simulated clients, most pharmacies (71%, 73 of 103) recommended alternative therapies and nutritional supplements such as vitamin C; the rest recommended conventional therapies such as antibiotics. While 52 (48%) of 108 pharmacies had at least one staff member trained on COVID-19, a general feeling of disconnection from the national programme prevailed.

CONCLUSIONS

Private pharmacies in Kenya were actively contributing to the COVID-19 response, but more deliberate engagement, support and linkages are required. Notably, there is an urgent need to develop guidelines for pharmacy-based COVID-19 testing, a service that is clearly needed and which could greatly increase test coverage. Pharmacy-based COVID-19 programmes should be accompanied with implementation research to inform current and future pandemic responses.

Early warning of COVID-19 hotspots using human mobility and web search query data

COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical interventions (e.g., lockdowns) through population density analysis. However, predicting the locations of potential outbreak occurrence is difficult using mobility data alone. Meanwhile, web search queries have been shown to be good predictors of the disease spread. In this study, we utilize a unique dataset of human mobility trajectories (GPS traces) and web search queries with common user identifiers (> 450 K users), to predict COVID-19 hotspot locations beforehand. More specifically, web search query analysis is conducted to identify users with high risk of COVID-19 contraction, and social contact analysis was further performed on the mobility patterns of these users to quantify the risk of an outbreak. Our approach is empirically tested using data collected from users in Tokyo, Japan. We show that by integrating COVID-19 related web search query analytics with social contact networks, we are able to predict COVID-19 hotspot locations 1-2 weeks beforehand, compared to just using social contact indexes or web search data analysis. This study proposes a novel method that can be used in early warning systems for disease outbreak hotspots, which can assist government agencies to prepare effective strategies to prevent further disease spread. Human mobility data and web search query data linked with common IDs are used to predict COVID-19 outbreaks. High risk social contact index captures both the contact density and COVID-19 contraction risks of individuals. Real world data was collected from 200 K individual users in Tokyo during the COVID-19 pandemic. Experiments showed that the index can be used for microscopic outbreak early warning.

Timing of non-pharmaceutical interventions to mitigate COVID-19 transmission and their effects on mobility: a cross-country analysis

In the early stages of a pandemic, non-pharmaceutical interventions (NPIs) that encourage physical distancing and reduce contact can decrease and delay disease transmission. Although NPIs have been implemented globally during the COVID-19 pandemic, their intensity and timing have varied widely. This paper analyzed the country-level determinants and effects of NPIs during the early stages of the pandemic (January 1st to April 29th, 2020). We examined countries that had implemented NPIs within 30 or 45 days since first case detection, as well as countries in which 30 or 45 days had passed since first case detection. The health and socioeconomic factors associated with delay in implementation of three NPIs-national school closure, national lockdown, and global travel ban-were analyzed using fractional logit and probit models, and beta regression models. The probability of implementation of national school closure, national lockdown, and strict national lockdown by a country was analyzed using a probit model. The effects of these three interventions on mobility changes were analyzed with propensity score matching methods using Google's social mobility reports. Countries with larger populations and better health preparedness measures had greater delays in implementation. Countries with greater population density, higher income, more democratic political systems, and later arrival of first cases were more likely to implement NPIs within 30 or 45 days of first case detection. Implementation of lockdowns significantly reduced physical mobility. Mobility was further reduced when lockdowns were enforced with curfews or fines, or when they were more strictly defined. National school closures did not significantly change mobility.

Impact of the coronavirus disease 2019 interventions on the incidence of hand, foot, and mouth disease in mainland China

Background

In early 2020, non-pharmaceutical interventions (NPIs) were implemented in China to reduce and contain the coronavirus disease 2019 (COVID-19) transmission. These NPIs might have also reduced the incidence of hand, foot, and mouth disease (HFMD).

Methods

The weekly numbers of HFMD cases and meteorological factors in 31 provincial capital cities and municipalities in mainland China were obtained from Chinese Center for Disease Control and Prevention (CCDC) and National Meteorological Information Center of China from 2016 to 2020. The NPI data were collected from local CDCs. The incidence rate ratios (IRRs) were calculated for the entire year of 2020, and for January-July 2020 and August-December 2020. The expected case numbers were estimated using seasonal autoregressive integrated moving average models. The relationships between kindergarten closures and incidence of HFMD were quantified using a generalized additive model. The estimated associations from all cities were pooled using a multivariate meta-regression model.

Findings

Stringent NPIs were widely implemented for COVID-19 control from January to July 2020, and the IRRs for HFMD were less than 1 in all 31 cities, and less than 0·1 for 23 cities. Overall, the proportion of HFMD cases reduced by 52·9% (95% CI: 49·3-55·5%) after the implementation of kindergarten closures in 2020, and this effect was generally consistent across subgroups.

Interpretation

The decrease in HFMD incidence was strongly associated with the NPIs for COVID-19. HFMD epidemic peaks were either absent or delayed, and the final epidemic size was reduced. Kindergarten closure is an intervention to prevent HFMD outbreaks.

Funding

This research was supported by the National Natural Science Foundation of China (81973102 & 81773487), Public Health Talents Training Program of Shanghai Municipality (GWV-10.2-XD21), the Shanghai New Three-year Action Plan for Public Health (GWV-10.1-XK16), the Major Project of Scientific and Technical Winter Olympics from National Key Research and Development Program of China (2021YFF0306000), 13th Five-Year National Science and Technology Major Project for Infectious Diseases (2018ZX10725-509) and Key projects of the PLA logistics Scientific research Program (BHJ17J013).

Optimal strategic pandemic control: human mobility and travel restriction

This paper presents a model for finding optimal pandemic control policy considering cross-region human mobility. We extend the baseline susceptible-infectious-recovered (SIR) epidemiology model by including the net human mobility from a severely-impacted region to a mildly-affected region. The strategic optimal mitigation policy combining testing and lockdown in each region is then obtained with the goal of minimizing economic cost under the constraint of limited resources. We parametrize the model using the data of the COVID-19 pandemic and show that the optimal response strategy and mitigation outcome greatly rely on the mitigation duration, available resources, and cross-region human mobility. Furthermore, we discuss the economic impact of travel restriction policies through a quantitative analysis.

A simulated measurement for COVID-19 pandemic using the effective reproductive number on an empirical portion of population: epidemiological models

COVID-19 as a global pandemic has had an unprecedented impact on the entire world. Projecting the future spread of the virus in relation to its characteristics for a specific suite of countries against a temporal trend can provide public health guidance to governments and organizations. Therefore, this paper presented an epidemiological comparison of the traditional SEIR model with an extended and modified version of the same model by splitting the infected compartment into asymptomatic mild and symptomatic severe. We then exposed our derived layered model into two distinct case studies with variations in mitigation strategies and non-pharmaceutical interventions (NPIs) as a matter of benchmarking and comparison. We focused on exploring the United Arab Emirates (a small yet urban centre (where clear sequential stages NPIs were implemented). Further, we concentrated on extending the models by utilizing the effective reproductive number (R t) estimated against time, a more realistic than the static R 0, to assess the potential impact of NPIs within each case study. Compared to the traditional SEIR model, the results supported the modified model as being more sensitive in terms of peaks of simulated cases and flattening determinations.

The timing and aggressiveness of early government response to COVID-19: Political systems, societal culture, and more

Factors that drove the early timing and strictness of government responses to COVID-19 for over 150 countries are examined using the daily Coronavirus Government Response Tracker data provided by the University of Oxford. Results show that authoritarian regimes tended to have an initial policy response somewhat weaker relative to democratic regimes at the early stages of the pandemic but pursed more aggressive containment policies over the latter part of the six-month period analyzed. Unitary regimes tended to have stronger policy measures in place early on relative to federalist states but relaxed these restrictions sooner. Countries with greater freedom (political rights and civil liberties) and those that spend less on public health also exhibited slower early policy responses, but caught up within three to four months after the pandemic reached their country. There is no evidence that women leaders, viewed as a whole, put in place more aggressive polices to combat the virus relative to their male counterparts. Nor is there any evidence that either island nations or countries that experienced the start of the pandemic later in the global wave pursued different policies that other nations. Policy implications are discussed as the how nations should prepare for future pandemics.

The city turned off: Urban dynamics during the COVID-19 pandemic based on mobile phone data

Due to the rapid expansion of the COVID-19 pandemic, many countries ordained lockdowns, establishing different restrictions on people's mobility. Exploring to what extent these measures have been effective is critical in order to better respond to similar future scenarios. This article uses anonymous mobile phone data to study the impact of the Spanish lockdown on the daily dynamics of the Madrid metropolitan area (Spain). The analysis has been carried out for a reference week prior to the lockdown and during several weeks of the lockdown in which different restrictions were in place. During these weeks, population distribution is compared during the day and at night and presence profiles are obtained throughout the day for each type of land use. In addition, a spatial multiple regression analysis is carried out to determine the impact of the different land uses on the local population. The results in the reference week, pre-COVID-19, show how the population in activity areas increases in each time slot on a specific day and how in residential areas it decreases. However, during the lockdown, activity areas cease to attract population during the day and the residential areas therefore no longer show a decrease. Only basic essential commercial activities, or others that require the presence of workers (industrial or logistics) maintain some activity during lockdown.

The Consequences of COVID-19 Toward Human Growth: The Role of Traumatic Event and Coping Strategies Among Indonesian Sample

COVID-19 has brought a massive psychological impact on individuals' life. The current study sets a significant purpose to test the model whether post-traumatic stress and coping strategies affect stress-related growth regarding the COVID-19 event. One hundred and ninety-nine participants have participated in an online survey in the period of lockdown. The proposed hypotheses model is further tested using PLS-SEM. The first model explains a significant moderate, 46% amount of variance for stress-related growth. With gender as moderator, the second model explains a significant 29% amount of variance for stress-related growth, which is also moderate. This study shows that active coping strategies and positive affirmation significantly influence individual stress-related growth. The trauma event (COVID-19) does not significantly affect growth. Women experience trauma compared to men, besides active coping with the COVID-19 situation is higher in men than women. Using the Bio-centric perspective, having a positive connection through acceptance and awareness of the situation, self-care, and affective interaction with others would develop growth regarding traumatic situations. Further, interventions about coping skills and positive affirmations are essential to give, especially to vulnerable groups such as women.

Onset of effects of non-pharmaceutical interventions on COVID-19 infection rates in 176 countries

BACKGROUND

During the initial phase of the global COVID-19 outbreak, most countries responded with non-pharmaceutical interventions (NPIs). In this study we investigate the general effectiveness of these NPIs, how long different NPIs need to be in place to take effect, and how long they should be in place for their maximum effect to unfold.

METHODS

We used global data and a non-parametric machine learning model to estimate the effects of NPIs in relation to how long they have been in place. We applied a random forest model and used accumulated local effect (ALE) plots to derive estimates of the effectiveness of single NPIs in relation to their implementation date. In addition, we used bootstrap samples to investigate the variability in these ALE plots.

RESULTS

Our results show that closure and regulation of schools was the most important NPI, associated with a pronounced effect about 10 days after implementation. Restrictions of mass gatherings and restrictions and regulations of businesses were found to have a more gradual effect, and social distancing was associated with a delayed effect starting about 18 days after implementation.

CONCLUSIONS

Our results can inform political decisions regarding the choice of NPIs and how long they need to be in place to take effect.

Real-time monitoring of COVID-19 dynamics using automated trend fitting and anomaly detection

As several countries gradually release social distancing measures, rapid detection of new localized COVID-19 hotspots and subsequent intervention will be key to avoiding large-scale resurgence of transmission. We introduce ASMODEE (automatic selection of models and outlier detection for epidemics), a new tool for detecting sudden changes in COVID-19 incidence. Our approach relies on automatically selecting the best (fitting or predicting) model from a range of user-defined time series models, excluding the most recent data points, to characterize the main trend in an incidence. We then derive prediction intervals and classify data points outside this interval as outliers, which provides an objective criterion for identifying departures from previous trends. We also provide a method for selecting the optimal breakpoints, used to define how many recent data points are to be excluded from the trend fitting procedure. The analysis of simulated COVID-19 outbreaks suggests ASMODEE compares favourably with a state-of-art outbreak-detection algorithm while being simpler and more flexible. As such, our method could be of wider use for infectious disease surveillance. We illustrate ASMODEE using publicly available data of National Health Service (NHS) Pathways reporting potential COVID-19 cases in England at a fine spatial scale, showing that the method would have enabled the early detection of the flare-ups in Leicester and Blackburn with Darwen, two to three weeks before their respective lockdown. ASMODEE is implemented in the free R package trendbreaker. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.

Impact of Nonpharmacological Public Health Interventions on Epidemiological Parameters of COVID-19 Pandemic in India

Background Public health interventions are epidemiologically sound and cost-effective methods to control disease burden. Non-pharmacological public health interventions are the only mode to control diseases in the absence of medication. Objective To find the impact of public health interventions on the epidemiological indicators of disease progression. Methods This is a secondary data analysis done on COVID-19 data. The median doubling time and R0 were calculated for a rolling period of seven days. Interventions were scored from zero to three with an increasing level of stringency. Multivariate linear regression was performed to find the role of individual interventions on R0 and the median doubling time. Results The highest intervention score was reported in the lockdown phase, which gradually decreased to the lowest level of 22. The R0 values settled to a level of 1.25, and the median doubling time increased to 20 days at the end of the study. Public awareness and public health laws were found to be related to both R0 and the median doubling time in the pre-lockdown phase only. Conclusion The implementation of interventions at the ground level is one of the key factors in the success of public health interventions. Post implementation, poor effectiveness of many interventions is evident from the study. Further, studies related to the sequence of interventions are required to further analyze the poor effect of the interventions.

Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City

The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.

Machine Learning-Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation

BACKGROUND

National governments worldwide have implemented nonpharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects.

OBJECTIVE

The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth-the percentage change in total cumulative cases-across 14 days for 114 countries using nonpharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national sociocultural norms.

METHODS

We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non-time series machine learning models in predicting confirmed infection growth. We used three validation methods-in-distribution, out-of-distribution, and country-based cross-validation-for the evaluation, each of which was applicable to a different use case of the models.

RESULTS

Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959) and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression. Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case.

CONCLUSIONS

This work provides new considerations in using machine learning techniques with nonpharmaceutical interventions and cultural dimensions as metrics to predict the national growth of confirmed COVID-19 infections.

Travel-related control measures to contain the COVID-19 pandemic: an evidence map

OBJECTIVES

To comprehensively map the existing evidence assessing the impact of travel-related control measures for containment of the SARS-CoV-2/COVID-19 pandemic.

DESIGN

Rapid evidence map.

DATA SOURCES

MEDLINE, Embase and Web of Science, and COVID-19 specific databases offered by the US Centers for Disease Control and Prevention and the WHO.

ELIGIBILITY CRITERIA

We included studies in human populations susceptible to SARS-CoV-2/COVID-19, SARS-CoV-1/severe acute respiratory syndrome, Middle East respiratory syndrome coronavirus/Middle East respiratory syndrome or influenza. Interventions of interest were travel-related control measures affecting travel across national or subnational borders. Outcomes of interest included infectious disease, screening, other health, economic and social outcomes. We considered all empirical studies that quantitatively evaluate impact available in Armenian, English, French, German, Italian and Russian based on the team's language capacities.

DATA EXTRACTION AND SYNTHESIS

We extracted data from included studies in a standardised manner and mapped them to a priori and (one) post hoc defined categories.

RESULTS

We included 122 studies assessing travel-related control measures. These studies were undertaken across the globe, most in the Western Pacific region (n=71). A large proportion of studies focused on COVID-19 (n=59), but a number of studies also examined SARS, MERS and influenza. We identified studies on border closures (n=3), entry/exit screening (n=31), travel-related quarantine (n=6), travel bans (n=8) and travel restrictions (n=25). Many addressed a bundle of travel-related control measures (n=49). Most studies assessed infectious disease (n=98) and/or screening-related (n=25) outcomes; we found only limited evidence on economic and social outcomes. Studies applied numerous methods, both inferential and descriptive in nature, ranging from simple observational methods to complex modelling techniques.

CONCLUSIONS

We identified a heterogeneous and complex evidence base on travel-related control measures. While this map is not sufficient to assess the effectiveness of different measures, it outlines aspects regarding interventions and outcomes, as well as study methodology and reporting that could inform future research and evidence synthesis.

Top-Down and Bottom-Up Lockdown: Evidence from COVID-19 Prevention and Control in China

Utilizing national migration data regarding the outbreak of the novel coronavirus (2019-nCoV), this paper employs a difference-in-differences approach to empirically analyze the relationship between human mobility and the transmission of infectious diseases in China. We show that national human mobility restrictions ascribed to the first-level public health emergency response policy effectively reduce both intercity and intracity migration intensities, thus leading to a declining scale of human mobility, which improves the effectiveness in controlling the epidemic. Human mobility restrictions have greater influences on cities with better economic development, denser populations, or larger passenger volumes. Moreover, mobility restriction measures are found to be better implemented in regions with increased public awareness, or with provincial leaders who have healthcare crisis management experience, local administrative experience, or the opportunity to serve a consecutive term.

Association Between the COVID-19 Pandemic and Infant Neurodevelopment: A Comparison Before and During COVID-19

Aim: To investigate the association between the experience of the coronavirus disease 2019 (COVID-19) pandemic and neurodevelopment of 6-month-old and 1-year-old children and explore the differences in the association by birth order. Methods: This comparison study was embedded in the Born in Guangzhou Cohort Study in China. The exposed group included 546 6-month-old and 285 1-year-old children who attended neurodevelopment assessments between March 1 and May 15, 2020, and the non-exposed group included 3,009 6-month-old and 2,214 1-year-old children during the same months from 2015 to 2019. Neurodevelopment at age 6 months and 1 year was assessed by trained clinical staff using the Ages and Stages Questionnaires, third edition (ASQ-3) and the Gesell Developmental Schedules (GDS). Results: The experience of the pandemic in 2020 was associated with a higher risk of delay in the fine motor (adjusted OR: 2.50, 95% CI: 1.25, 4.99; estimated by logistic regression) and communication (adjusted RR [aRR]: 1.13, 95% CI: 1.02, 1.25; estimated by log-binomial regression) domains at age 1 year. The association between the experience of the pandemic and communication delay at age 1 year only existed in first-born children (aRR: 1.15, 95% CI: 1.03, 1.30) but not in later-born children (aRR: 1.02, 95% CI: 0.84, 1.25). No associations were observed in any domain among 6-month-olds. Conclusion: Experiencing the COVID-19 pandemic and related public health strategies might be associated with a higher risk of delay in the development of fine motor and communication in 1-year-old children; the association observed in the communication domain only existed in first-born children.

Mathematical perspective of Covid-19 pandemic: Disease extinction criteria in deterministic and stochastic models

The world has been facing the biggest virological invasion in the form of Covid-19 pandemic since the beginning of the year 2020. In this paper, we consider a deterministic epidemic model of four compartments based on the health status of the populations of a given country to capture the disease progression. A stochastic extension of the deterministic model is further considered to capture the uncertainty or variation observed in the disease transmissibility. In the case of a deterministic system, the disease-free equilibrium will be globally asymptotically stable if the basic reproduction number is less than unity, otherwise, the disease persists. Using Lyapunov functional methods, we prove that the infected population of the stochastic system tends to zero exponentially almost surely if the basic reproduction number is less than unity. The stochastic system has no interior equilibrium, however, its asymptotic solution is shown to fluctuate around the endemic equilibrium of the deterministic system under some parametric restrictions, implying that the infection persists. A case study with the Covid-19 epidemic data of Spain is presented and various analytical results have been demonstrated. The epidemic curve in Spain clearly shows two waves of infection. The first wave was observed during March-April and the second wave started in the middle of July and not completed yet. A real-time reproduction number has been given to illustrate the epidemiological status of Spain throughout the study period. Estimated cumulative numbers of confirmed and death cases are 1,613,626 and 42,899, respectively, with case fatality rate 2.66% till the deadly virus is eliminated from Spain.

Effect of COVID-19 Non-Pharmaceutical Interventions and the Implications for Human Rights

In response to the COVID-19 pandemic, many governments swiftly decided to order nationwide lockdowns based on limited evidence that such extreme measures were effective in containing the epidemic. A growing concern is that governments were given little time to adopt effective and proportional interventions protecting citizens' lives while observing their freedom and rights. This paper examines the effectiveness of non-pharmaceutical interventions (NPIs) in containing COVID-19, by conducting a linear regression over 108 countries, and the implication for human rights. The regression results are supported by evidence that shows the change in 10 selected countries' responding strategies and their effects as the confirmed cases increase. We found that school closures are effective in containing COVID-19 only when they are implemented along with complete contact tracing. Our findings imply that to contain COVID-19 effectively and minimize the risk of human rights abuses, governments should consider implementing prudently designed full contact tracing and school closure policies, among others. Minimizing the risk of human rights abuses should be a principle even when full contact tracing is implemented.

State-specific projection of COVID-19 infection in the United States and evaluation of three major control measures

Most models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are most effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing.

Public health response to COVID-19 in selected countries - Hits and misses

Background

Lack of a cure or vaccine of COVID-19 forced us to rely on public health interventions (PHIs) for combating the pandemic. The main objective of the study to assess the PHI in selected countries and relate the various factors related to the intervention with the case load of the country.

Methods

An ecological analysis was conducted using secondary data on PHIs and disease burden extracted from official documents and press releases of the respective countries. Disease transmission was described based on calculated doubling time. PHIs were classified into 14 categories within three domains. An intervention score was calculated to reflect the number and stringency of the PHIs. Correlations between intervention scores, daily new cases and doubling time were presented.

Results

Brazil and the USA had the lowest intervention scores while South Korea had the highest scores. The median doubling time was negatively correlated with the rapidity of the escalation of the PHIs.

Conclusion

Dynamic government policies and timely PHIs, which are locally relevant and ably supported by the public are key to successful containment of the COVID-19 pandemic.

Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map

Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public. Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review. Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors. Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves.

COVID-19: A review of the effectiveness of non-pharmacological interventions

COVID-19, a highly infectious disease, caused by a novel virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has brought about an unprecedented threat to global health. First reported in Wuhan, China, in December 2019, it has now spread to all continents of the world becoming a pandemic. There is no known treatment or vaccine for it although many candidate drugs and vaccines are in various clinical trial phases. For now, non-pharmacological interventions (NPIs) have become the mainstay of response for COVID-19 and are being used across the world to flatten the epidemiologic curve with some success. This review focussed on identifying which NPIs have been effective. NPIs that are effective include isolation and quarantine, physical distancing, use of face masks and hand hygiene. These measures are best used in combination and simultaneously. The evidence is that they should be instituted early in the pandemic and for sustained periods. They should also be implemented in the context of the cultural and socioeconomic conditions of the populace. Ineffective NPIs include ultraviolet irradiation and spraying of outdoor spaces and individuals. We recommend that decision makers weigh the evidence carefully, as it applies to the local setting to inform public health decisions.

Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China

We quantify the causal impact of human mobility restrictions, particularly the lockdown of Wuhan on January 23, 2020, on the containment and delay of the spread of the Novel Coronavirus (2019-nCoV). We employ difference-in-differences (DID) estimations to disentangle the lockdown effect on human mobility reductions from other confounding effects including panic effect, virus effect, and the Spring Festival effect. The lockdown of Wuhan reduced inflows to Wuhan by 76.98%, outflows from Wuhan by 56.31%, and within-Wuhan movements by 55.91%. We also estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities - the epicenter of the 2019-nCoV outbreak - on the destination cities' new infection cases. We also provide evidence that the enhanced social distancing policies in the 98 Chinese cities outside Hubei province were effective in reducing the impact of the population inflows from the epicenter cities in Hubei province on the spread of 2019-nCoV in the destination cities. We find that in the counterfactual world in which Wuhan were not locked down on January 23, 2020, the COVID-19 cases would be 105.27% higher in the 347 Chinese cities outside Hubei province. Our findings are relevant in the global efforts in pandemic containment.

Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China

We quantify the causal impact of human mobility restrictions, particularly the lockdown of Wuhan on January 23, 2020, on the containment and delay of the spread of the Novel Coronavirus (2019-nCoV). We employ difference-in-differences (DID) estimations to disentangle the lockdown effect on human mobility reductions from other confounding effects including panic effect, virus effect, and the Spring Festival effect. The lockdown of Wuhan reduced inflows to Wuhan by 76.98%, outflows from Wuhan by 56.31%, and within-Wuhan movements by 55.91%. We also estimate the dynamic effects of up to 22 lagged population inflows from Wuhan and other Hubei cities - the epicenter of the 2019-nCoV outbreak - on the destination cities' new infection cases. We also provide evidence that the enhanced social distancing policies in the 98 Chinese cities outside Hubei province were effective in reducing the impact of the population inflows from the epicenter cities in Hubei province on the spread of 2019-nCoV in the destination cities. We find that in the counterfactual world in which Wuhan were not locked down on January 23, 2020, the COVID-19 cases would be 105.27% higher in the 347 Chinese cities outside Hubei province. Our findings are relevant in the global efforts in pandemic containment.

Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic

While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

Early school closures can reduce the first-wave of the COVID-19 pandemic development

Aim

The COVID-19 pandemic presents serious threats to global public health and the world economy. Therefore, the rapid escalation of the number of cases has led to national government and global interventions. This study aimed to assess the effect of school closures on the COVID-19 pandemic and epidemic trajectories in selected countries.

Subject and methods

Information on the number of cases and population in each country were taken from official government reports. Dates of educational institutions closure were taken from the UNESCO database. Statistical analyses were performed using Statistica. We summarized the data graphically and descriptively.

Results

Most of the European countries closed schools in the period of 11–20 of March 2020. However, there was a big difference in the phase of the epidemic on the day of closure. The data indicate that there was a strong correlation between the day of educational facilities closure and the incidence rate in the following days (16th, 30th, and 60th days since the 100th confirmed case in each country). Early closure of schools in analyzed countries is statistically significantly correlated with lower incidence rates further on during the different phases of the epidemic. Thereby closure of schools with delay is statistically significantly correlated with a higher incidence rate in the following days.

Conclusion

The available data suggest that school closures can potentially reduce transmission during the pandemic, although more research is needed on the effectiveness of these practices.

The need for early referral to palliative care especially for Black, Asian and minority ethnic groups in a COVID-19 pandemic: Findings from a service evaluation

BACKGROUND

Palliative care services face challenges in adapting and responding to the COVID-19 pandemic. Understanding how palliative care needs and outcomes have changed during the pandemic compared to before the pandemic is crucial to inform service planning and research initiatives.

AIM

To evaluate the impact of COVID-19 on symptoms, clinical characteristics, and outcomes for patients referred to a hospital-based palliative care service in a district general hospital in London, UK.

DESIGN

A retrospective service evaluation. Data were extracted from the electronic patient records.

SETTING/PARTICIPANTS

The first 60 inpatients with confirmed COVID-19 infection, referred to the hospital palliative care service between 1 March 2020 and 23 April 2020, and another 60 inpatients, referred to the hospital palliative care service between 11 March 2019 and 23 April 2019, were included from a district general hospital in East London, UK.

RESULTS

Patients with COVID-19 have lower comorbidity scores, poorer performance status, and a shorter time from referral to death compared to patients without COVID-19. Breathlessness, drowsiness, agitation, and fever are the most prevalent symptoms during COVID-19 compared to pain and drowsiness pre-COVID-19. Time from admission to referral to palliative care is longer for Black, Asian and minority ethnic patients, especially during COVID-19.

CONCLUSION

Early referral to palliative care is essential in COVID-19, especially for Black, Asian and minority ethnic groups. There is urgent need to research why Black, Asian and minority ethnic patients are referred late; how palliative care services have changed; and possible solutions to setting up responsive, flexible, and integrated services.

Strong impact of closing schools, closing bars and wearing masks during the Covid-19 pandemic: results from a simple and revealing analysis

Many complex mathematical and epidemiological methods have been used to model the Covid-19 pandemic. Among other results from these models has been the view that closing schools had little impact on infection rates in several countries1. We took a different approach. Making one assumption, we simply plotted cases, hospitalizations and deaths, on a log2 Y axis and a linear date-based X axis, and analyzed them using segmented regression, a powerful method that has largely been overlooked during this pandemic. Here we show that the data fit straight lines with correlation coefficients ranging from 92% - 99%, and that these lines broke at interesting intervals, revealing that school closings dropped infection rates in half, lockdowns dropped the rates 3 to 4 fold, and other actions (such as closing bars and mandating masks) brought the rates even further down. Hospitalizations and deaths paralleled cases, with lags of three to ten days. The graphs, which are easy to read, reveal changes in infection rates that are not obvious using other graphing methods, and have several implications for modeling and policy development during this and future pandemics. Overall, other than full lockdowns, three interventions had the most impact: closing schools, closing bars and wearing masks: a message easily understood by the public.

COVID-19 open source data sets: a comprehensive survey

In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.

Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries

BACKGROUND

As governments across Europe have issued non-pharmaceutical interventions (NPIs) such as social distancing and school closing, the mobility patterns in these countries have changed. Most states have implemented similar NPIs at similar time points. However, it is likely different countries and populations respond differently to the NPIs and that these differences cause mobility patterns and thereby the epidemic development to change.

METHODS

We build a Bayesian model that estimates the number of deaths on a given day dependent on changes in the basic reproductive number, R 0, due to differences in mobility patterns. We utilise mobility data from Google mobility reports using five different categories: retail and recreation, grocery and pharmacy, transit stations, workplace and residential. The importance of each mobility category for predicting changes in R 0 is estimated through the model.

FINDINGS

The changes in mobility have a considerable overlap with the introduction of governmental NPIs, highlighting the importance of government action for population behavioural change. The shift in mobility in all categories shows high correlations with the death rates 1 month later. Reduction of movement within the grocery and pharmacy sector is estimated to account for most of the decrease in R 0.

INTERPRETATION

Our model predicts 3-week epidemic forecasts, using real-time observations of changes in mobility patterns, which can provide governments with direct feedback on the effects of their NPIs. The model predicts the changes in a majority of the countries accurately but overestimates the impact of NPIs in Sweden and Denmark and underestimates them in France and Belgium. We also note that the exponential nature of all epidemiological models based on the basic reproductive number, R 0 cause small errors to have extensive effects on the predicted outcome.

Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions

The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the USA and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until at least summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.

The effect of travel restrictions on the geographical spread of COVID-19 between large cities in China: a modelling study

BACKGROUND

To contain the spread of COVID-19, a cordon sanitaire was put in place in Wuhan prior to the Lunar New Year, on 23 January 2020. We assess the efficacy of the cordon sanitaire to delay the introduction and onset of local transmission of COVID-19 in other major cities in mainland China.

METHODS

We estimated the number of infected travellers from Wuhan to other major cities in mainland China from November 2019 to February 2020 using previously estimated COVID-19 prevalence in Wuhan and publicly available mobility data. We focused on Beijing, Chongqing, Hangzhou, and Shenzhen as four representative major cities to identify the potential independent contribution of the cordon sanitaire and holiday travel. To do this, we simulated outbreaks generated by infected arrivals in these destination cities using stochastic branching processes. We also modelled the effect of the cordon sanitaire in combination with reduced transmissibility scenarios to simulate the effect of local non-pharmaceutical interventions.

RESULTS

We find that in the four cities, given the potentially high prevalence of COVID-19 in Wuhan between December 2019 and early January 2020, local transmission may have been seeded as early as 1-8 January 2020. By the time the cordon sanitaire was imposed, infections were likely in the thousands. The cordon sanitaire alone did not substantially affect the epidemic progression in these cities, although it may have had some effect in smaller cities. Reduced transmissibility resulted in a notable decrease in the incidence of infection in the four studied cities.

CONCLUSIONS

Our results indicate that sustained transmission was likely occurring several weeks prior to the implementation of the cordon sanitaire in four major cities of mainland China and that the observed decrease in incidence was likely attributable to other non-pharmaceutical, transmission-reducing interventions.

Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

BACKGROUND

The inherent difficulty of identifying and monitoring emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing coronavirus disease (COVID-19) outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events.

OBJECTIVE

We present a timely and novel methodology that combines disease estimates from mechanistic models and digital traces, via interpretable machine learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real time.

METHODS

Our method uses the following as inputs: (a) official health reports, (b) COVID-19-related internet search activity, (c) news media activity, and (d) daily forecasts of COVID-19 activity from a metapopulation mechanistic model. Our machine learning methodology uses a clustering technique that enables the exploitation of geospatial synchronicities of COVID-19 activity across Chinese provinces and a data augmentation technique to deal with the small number of historical disease observations characteristic of emerging outbreaks.

RESULTS

Our model is able to produce stable and accurate forecasts 2 days ahead of the current time and outperforms a collection of baseline models in 27 out of 32 Chinese provinces.

CONCLUSIONS

Our methodology could be easily extended to other geographies currently affected by COVID-19 to aid decision makers with monitoring and possibly prevention.

The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China

Non-pharmaceutical interventions to control COVID-19 spread have been implemented in several countries with different intensity, timing, and impact on transmission. As a result, post-lockdown COVID-19 dynamics are heterogenous and difficult to interpret. Here we describe a set of contact surveys performed in four Chinese cities (Wuhan, Shanghai, Shenzhen, and Changsha) during the pre-pandemic, lockdown, and post-lockdown period to quantify the transmission impact of relaxing interventions via changes in age-specific contact patterns. We estimate that the mean number of contacts increased 5%-17% since the end of the lockdown but are still 3-7 times lower than their pre-pandemic levels. We find that post-lockdown contact patterns in China are still sufficiently low to keep SARS-CoV-2 transmission under control. We also find that the impact of school interventions depends non-linearly on the share of other activities being resumed. When most community activities are halted, school closure leads to a 77% decrease in the reproductive number; in contrast, when social mixing outside of schools is at pre-pandemic level, school closure leads to a 5% reduction in transmission. Moving forward, to control COVID-19 spread without resorting to a lockdown, it will be key to dose relaxation in social mixing in the community and strengthen targeted interventions.

[The outbreak of COVID-19 in China]

The transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan and Hubei Province differ considerably from those in the rest of China. In Hubei province SARS-CoV‑2 led to a dramatic outbreak. Intensive control measures (travel restrictions, isolation of cases, quarantine of contacts and others) led to the control of the outbreak. Despite travel restrictions SARS-CoV‑2 was detected in other provinces in the following weeks. Consistent and intensive identification and isolation of infected persons ("containment") was able to prevent an outbreak outside Hubei province, providing an example for the control of SARS-CoV‑2.

COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown

Italy has been severely affected by the COVID-19 pandemic, reporting the highest death toll in Europe as of April 2020. Following the identification of the first infections, on February 21, 2020, national authorities have put in place an increasing number of restrictions aimed at containing the outbreak and delaying the epidemic peak. On March 12, the government imposed a national lockdown. To aid the evaluation of the impact of interventions, we present daily time-series of three different aggregated mobility metrics: the origin-destination movements between Italian provinces, the radius of gyration, and the average degree of a spatial proximity network. All metrics were computed by processing a large-scale dataset of anonymously shared positions of about 170,000 de-identified smartphone users before and during the outbreak, at the sub-national scale. This dataset can help to monitor the impact of the lockdown on the epidemic trajectory and inform future public health decision making.

Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis

BACKGROUND

Nonpharmaceutical interventions (NPIs) are the primary tools to mitigate early spread of the coronavirus disease 2019 (COVID-19) pandemic; however, such policies are implemented variably at the federal, provincial or territorial, and municipal levels without centralized documentation. We describe the development of the comprehensive open Canadian Non-Pharmaceutical Intervention (CAN-NPI) data set, which identifies and classifies all NPIs implemented in regions across Canada in response to COVID-19, and provides an accompanying description of geographic and temporal heterogeneity.

METHODS

We performed an environmental scan of government websites, news media and verified government social media accounts to identify NPIs implemented in Canada between Jan. 1 and Apr. 19, 2020. The CAN-NPI data set contains information about each intervention's timing, location, type, target population and alignment with a response stringency measure. We conducted descriptive analyses to characterize the temporal and geographic variation in early NPI implementation.

RESULTS

We recorded 2517 NPIs grouped in 63 distinct categories during this period. The median date of NPI implementation in Canada was Mar. 24, 2020. Most jurisdictions heightened the stringency of their response following the World Health Organization's global pandemic declaration on Mar. 11, 2020. However, there was variation among provinces or territories in the timing and stringency of NPI implementation, with 8 out of 13 provinces or territories declaring a state of emergency by Mar. 18, and all by Mar. 22, 2020.

INTERPRETATION

There was substantial geographic and temporal heterogeneity in NPI implementation across Canada, highlighting the importance of a subnational lens in evaluating the COVID-19 pandemic response. Our comprehensive open-access data set will enable researchers to conduct robust interjurisdictional analyses of NPI impact in curtailing COVID-19 transmission.

The impact of government measures and human mobility trend on COVID-19 related deaths in the UK

The COVID-19 global pandemic has rapidly expanded, with the UK being one of the countries with the highest number of cases and deaths in proportion to its population. Major clinical and human behavioural measures have been taken by the UK government to control the spread of the pandemic and to support the health system. It remains unclear how exactly human mobility restrictions have affected the virus spread in the UK. This research uses driving, walking and transit real-time data to investigate the impact of government control measures on human mobility reduction, as well as the connection between trends in human-mobility and severe COVID-19 outcomes. Human mobility was observed to gradually decrease as the government was announcing more measures and it stabilized at a scale of around 80% after a lockdown was imposed. The study shows that human-mobility reduction had a significant impact on reducing COVID-19-related deaths, thus providing crucial evidence in support of such government measures.

Non-pharmaceutical interventions for containment, mitigation and suppression of COVID-19 infection

BACKGROUND

The best scientific evidence is required to design effective Non-pharmaceutical interventions to help policymakers to contain COVID-19.

AIM

To describe which Non-pharmaceutical interventions used different countries and a when they use them. It also explores how Non-pharmaceutical interventions impact the number of cases, the mortality, and the capacity of health systems.

METHODS

We consulted eight web pages of transnational organizations, 17 of international media, 99 of government institutions in the 19 countries included, and besides, we included nine studies (out of 34 identified) that met inclusion criteria.

RESULT

Some countries are focused on establishing travel restrictions, isolation of identified cases, and high-risk people. Others have a combination of mandatory quarantine and other drastic social distancing measures. The timing to implement the interventions varied from the first fifteen days after detecting the first case to more than 30 days. The effectiveness of isolated non-pharmaceutical interventions may be limited, but combined interventions have shown to be effective in reducing the transmissibility of the disease, the collapse of health care services, and mortality. When the number of new cases has been controlled, it is necessary to maintain social distancing measures, self-isolation, and contact tracing for several months. The policy decision-making in this time should be aimed to optimize the opportunities of saving lives, reducing the collapse of health services, and minimizing the economic and social impact over the general population, but principally over the most vulnerable. The timing of implementing and lifting interventions could have a substantial effect on those objectives.