The effect of human mobility restrictions on the COVID-19 transmission network in China

Uncovering COVID-19 transmission tree: identifying traced and untraced infections in an infection network

Introduction

This paper presents a comprehensive analysis of COVID-19 transmission dynamics using an infection network derived from epidemiological data in South Korea, covering the period from January 3, 2020, to July 11, 2021. The network illustrates infector-infectee relationships and provides invaluable insights for managing and mitigating the spread of the disease. However, significant missing data hinder conventional analysis of such networks from epidemiological surveillance.

Methods

To address this challenge, this article suggests a novel approach for categorizing individuals into four distinct groups, based on the classification of their infector or infectee status as either traced or untraced cases among all confirmed cases. The study analyzes the changes in the infection networks among untraced and traced cases across five distinct periods.

Results

The four types of cases emphasize the impact of various factors, such as the implementation of public health strategies and the emergence of novel COVID-19 variants, which contribute to the propagation of COVID-19 transmission. One of the key findings is the identification of notable transmission patterns in specific age groups, particularly in those aged 20-29, 40-69, and 0-9, based on the four type classifications. Furthermore, we develop a novel real-time indicator to assess the potential for infectious disease transmission more effectively. By analyzing the lengths of connected components, this indicator facilitates improved predictions and enables policymakers to proactively respond, thereby helping to mitigate the effects of the pandemic on global communities.

Conclusion

This study offers a novel approach to categorizing COVID-19 cases, provides insights into transmission patterns, and introduces a real-time indicator for better assessment and management of the disease transmission, thereby supporting more effective public health interventions.

Analyzing the Robustness of Complex Networks with Attack Success Rate

Analyzing the robustness of networks against random failures or malicious attacks is a critical research issue in network science, as it contributes to enhancing the robustness of beneficial networks and effectively dismantling harmful ones. Most studies commonly neglect the impact of the attack success rate (ASR) and assume that attacks on the network will always be successful. However, in real-world scenarios, attacks may not always succeed. This paper proposes a novel robustness measure called Robustness-ASR (RASR), which utilizes mathematical expectations to assess network robustness when considering the ASR of each node. To efficiently compute the RASR for large-scale networks, a parallel algorithm named PRQMC is presented, which leverages randomized quasi-Monte Carlo integration to approximate the RASR with a faster convergence rate. Additionally, a new attack strategy named HBnnsAGP is introduced to better assess the lower bound of network RASR. Finally, the experimental results on six representative real-world complex networks demonstrate the effectiveness of the proposed methods compared with the state-of-the-art baselines.

Policy evaluation during a pandemic

National and local governments have implemented a large number of policies in response to the Covid-19 pandemic. Evaluating the effects of these policies, both on the number of Covid-19 cases as well as on other economic outcomes is a key ingredient for policymakers to be able to determine which policies are most effective as well as the relative costs and benefits of particular policies. In this paper, we consider the relative merits of common identification strategies that exploit variation in the timing of policies across different locations by checking whether the identification strategies are compatible with leading epidemic models in the epidemiology literature. We argue that unconfoundedness type approaches, that condition on the pre-treatment "state" of the pandemic, are likely to be more useful for evaluating policies than difference-in-differences type approaches due to the highly nonlinear spread of cases during a pandemic. For difference-in-differences, we further show that a version of this problem continues to exist even when one is interested in understanding the effect of a policy on other economic outcomes when those outcomes also depend on the number of Covid-19 cases. We propose alternative approaches that are able to circumvent these issues. We apply our proposed approach to study the effect of state level shelter-in-place orders early in the pandemic.

Spatial dynamics of synergistic coinfection in rock-paper-scissors models

We investigate the spatial dynamics of two-disease epidemics reaching a three-species cyclic model. Regardless of their species, all individuals are susceptible to being infected with two different pathogens, which spread through person-to-person contact. We consider that the simultaneous presence of multiple infections leads to a synergistic amplification in the probability of host mortality due to complications arising from any of the co-occurring diseases. Employing stochastic simulations, we explore the ramifications of this synergistic coinfection on spatial configurations that emerge from stochastic initial conditions. Under conditions of pronounced synergistic coinfection, we identify the emergence of zones inhabited solely by hosts affected by a singular pathogen. At the boundaries of spatial domains dominated by a single disease, interfaces of coinfected hosts appear. The dynamics of these interfaces are shaped by curvature-driven processes and display a scaling behavior reflective of the topological attributes of the underlying two-dimensional space. As the lethality linked to coinfection diminishes, the evolution of the interface network's spatial dynamics is influenced by fluctuations stemming from waves of coinfection that infiltrate territories predominantly occupied by a single disease. Our analysis extends to quantifying the implications of synergistic coinfection at both the individual and population levels Our outcomes show that organisms' infection risk is maximized if the coinfection increases the death due to disease by 30% and minimized as the network dynamics reach the scaling regime, with species populations being maximum. Our conclusions may help ecologists understand the dynamics of epidemics and their impact on the stability of ecosystems.

Understanding the impact of mobility on COVID-19 spread: A hybrid gravity-metapopulation model of COVID-19

The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.

Cross-regional analysis of the association between human mobility and COVID-19 infection in Southeast Asia during the transitional period of “living with COVID-19”

Background

In response to COVID-19, Southeast Asian (SEA) countries had imposed stringent lockdowns and restrictions to mitigate the pandemic ever since 2019. Because of a gradually boosting vaccination rate along with a strong demand for economic recovery, many governments have shifted the intervention strategy from restrictions to “Living with COVID-19” where people gradually resumed their normal activities since the second half of the year 2021. Noticeably, timelines for enacting the loosened strategy varied across Southeast Asian countries, which resulted in different patterns of human mobility across space and time. This thus presents an opportunity to study the relationship between mobility and the number of infection cases across regions, which could provide support for ongoing interventions in terms of effectiveness.

Objective

This study aimed to investigate the association between human mobility and COVID-19 infections across space and time during the transition period of shifting strategies from restrictions to normal living in Southeast Asia. Our research results have significant implications for evidence-based policymaking at the present of the COVID-19 pandemic and other public health issues.

Methods

We aggregated weekly average human mobility data derived from the Facebook origin and destination Movement dataset. and weekly average new cases of COVID-19 at the district level from 01-Jun-2021 to 26-Dec-2021 (a total of 30 weeks). We mapped the spatiotemporal dynamics of human mobility and COVID-19 cases across countries in SEA. We further adopted the Geographically and Temporally Weighted Regression model to identify the spatiotemporal variations of the association between human mobility and COVID-19 infections over 30 weeks. Our model also controls for socioeconomic status, vaccination, and stringency of intervention to better identify the impact of human mobility on COVID-19 spread.

Results

The percentage of districts that presented a statistically significant association between human mobility and COVID-19 infections generally decreased from 96.15% in week 1 to 90.38% in week 30, indicating a gradual disconnection between human mobility and COVID-19 spread. Over the study period, the average coefficients in 7 SEA countries increased, decreased, and finally kept stable. The association between human mobility and COVID-19 spread also presents spatial heterogeneity where higher coefficients were mainly concentrated in districts of Indonesia from week 1 to week 10 (ranging from 0.336 to 0.826), while lower coefficients were mainly located in districts of Vietnam (ranging from 0.044 to 0.130). From week 10 to week 25, higher coefficients were mainly observed in Singapore, Malaysia, Brunei, north Indonesia, and several districts of the Philippines. Despite the association showing a general weakening trend over time, significant positive coefficients were observed in Singapore, Malaysia, western Indonesia, and the Philippines, with the relatively highest coefficients observed in the Philippines in week 30 (ranging from 0.101 to 0.139).

Conclusions

The loosening interventions in response to COVID-19 in SEA countries during the second half of 2021 led to diverse changes in human mobility over time, which may result in the COVID-19 infection dynamics. This study investigated the association between mobility and infections at the regional level during the special transitional period. Our study has important implications for public policy interventions, especially at the later stage of a public health crisis.

Impact of network centrality and income on slowing infection spread after outbreaks

The COVID-19 pandemic has shed light on how the spread of infectious diseases worldwide are importantly shaped by both human mobility networks and socio-economic factors. However, few studies look at how both socio-economic conditions and the complex network properties of human mobility patterns interact, and how they influence outbreaks together. We introduce a novel methodology, called the Infection Delay Model, to calculate how the arrival time of an infection varies geographically, considering both effective distance-based metrics and differences in regions' capacity to isolate-a feature associated with socio-economic inequalities. To illustrate an application of the Infection Delay Model, this paper integrates household travel survey data with cell phone mobility data from the São Paulo metropolitan region to assess the effectiveness of lockdowns to slow the spread of COVID-19. Rather than operating under the assumption that the next pandemic will begin in the same region as the last, the model estimates infection delays under every possible outbreak scenario, allowing for generalizable insights into the effectiveness of interventions to delay a region's first case. The model sheds light on how the effectiveness of lockdowns to slow the spread of disease is influenced by the interaction of mobility networks and socio-economic levels. We find that a negative relationship emerges between network centrality and the infection delay after a lockdown, irrespective of income. Furthermore, for regions across all income and centrality levels, outbreaks starting in less central locations were more effectively slowed by a lockdown. Using the Infection Delay Model, this paper identifies and quantifies a new dimension of disease risk faced by those most central in a mobility network.

Spatial organisation plasticity reduces disease infection risk in rock-paper-scissors models

We study a three-species cyclic game system where organisms face a contagious disease whose virulence may change by a pathogen mutation. As a responsive defence strategy, organisms' mobility is restricted to reduce disease dissemination in the system. The impact of the collective self-preservation strategy on the disease infection risk is investigated by performing stochastic simulations of the spatial version of the rock-paper-scissors game. Our outcomes show that the mobility control strategy induces plasticity in the spatial patterns with groups of organisms of the same species inhabiting spatial domains whose characteristic length scales depend on the level of dispersal restrictions. The spatial organisation plasticity allows the ecosystems to adapt to minimise the individuals' disease contamination risk if an eventual pathogen alters the disease virulence. We discover that if a pathogen mutation makes the disease more transmissible or less lethal, the organisms benefit more if the mobility is not strongly restricted, thus forming large spatial domains. Conversely, the benefits of protecting against a pathogen causing a less contagious or deadlier disease are maximised if the average size of groups of individuals of the same species is significantly limited, reducing the dimensions of groups of organisms significantly. Our findings may help biologists understand the effects of dispersal control as a conservation strategy in ecosystems affected by epidemic outbreaks.

EFFECTS OF RESTRICTION MEASURES ON MORBIDITY AND MORTALITY IMPLEMENTED DURING COVID-19 PANDEMIC IN TURKEY: A RESEARCH THROUGH NATIONAL DATA INCLUDING ONE YEAR

This research is aimed to determine effects of restriction measures implemented in Turkey during COVID 19 pandemic throughout detecting variations in the “number of cases daily”, “test positivity rate daily”, and “number of death daily” according to different restriction periods. In order to be able to analyze on the basis of cases declared as standard, the periods of restriction measures between November 18, 2020 and November 17, 2021 were included in the research. The data of the Ministry of Health was used as the source. When making statistical assessment for the "number of cases per day" and the "test positivity rate per day", we evaluated each restriction period to cover the first 10 days after the end of this period. When comparing the “daily death numbers”, we evaluated each restriction period to include the daily death numbers for the first 21 days after the end of that period. The highest means were seen for all three parameters examined during “revised local decision-making phase”. These mean are 57,396 for number of cases per day, 18.4 for test positivity rate per day, 351 for number of deaths per day. This period is the only period in which the means for "number of cases" and "number of deaths" are higher than the first period, which is the reference period, and for these parameters, a statistically significant difference is detected with the reference period (p

A community-level study on COVID-19 transmission and policy interventions in Wuhan, China

The specific factors and response strategies that affect COVID-19 transmission in local communities remain under-explored in the current literature due to a lack of data. Based on primary COVID-19 data collected at the community level in Wuhan, China, our study contributes a community-level investigation on COVID-19 transmission and response strategies by addressing two research questions: 1) What community factors are associated with viral transmission? and 2) What are the key mechanisms behind policy interventions towards controlling viral transmission within local communities? We conducted two sets of analyses to address these two questions-quantitative analyses of the relationship between community factors and viral transmission and qualitative analyses of policy interventions on community transmission. Our findings show that the viral spread in local communities is irrelevant to the built environment of a community and its socioeconomic position but is related to its demographic composition. Specifically, groups under the age of 18 play an important role in viral transmission. Moreover, a series of community shutdown management initiatives (e.g., group buying, delivering supplies, and self-reporting of health conditions) play an important role in curbing viral transmission at the local level that can be applied to other geographic contexts.

Exploring the impact of mobility restrictions on the COVID-19 spreading through an agent-based approach

BACKGROUND

The recent health emergency caused by the COVID-19 pandemic forced people to change their mobility habits, with the reduction of non-essential travels and the promotion online activities. During the first phase of the emergency in 2020, governments considered several mobility restrictions to avoid the pandemic diffusion. However, it is difficult to quantify the actual effects of these restrictions on the virus spreading, especially due to the biased data available. Notwithstanding the big role of data analysis to understand the pandemic phenomenon, it is also important to have more general models capable of predicting the impact of different policy scenarios, including territorial parameters, independently from the available infection data. In this respect, this paper proposes an agent-based model to simulate the impact of mobility restrictions on the spreading of the COVID-19 at a large scale level, by considering different factors that can be attributed to the diffusion and lethality of the virus and population mobility patterns.

METHODS

The first step of the method includes a zonation of the study area, according to administrative boundaries. A risk index is calculated for each zone considering indicators which can influence the virus spreading and people lethality: mean winter temperature, housing concentration, healthcare density, population mobility, air pollution and the percentage of population over 60 years old. The agent-based model associates the risk index to the agents and determines their "status" ("susceptible", "infected", "isolated", "recovered" or "dead") by combining the risk index with the mean infection duration, using a SIR-based approach (i.e. susceptible-infective-removed).

RESULTS

The study is applied to Italy. Several scenarios based on different mobility restrictions have been simulated, including the one based on the official data (status quo). The main results show that characterizing zones with a risk index allows to adopt local policies with almost the same effectiveness as in the case of restrictions extended to the full study area; scenario simulations return an increase in terms of infected (+20%) and deaths (+25%) with respect to the status quo. These results underline the importance of finding a trade-off between socio-economic benefits and health impact.

CONCLUSIONS

The reproducibility of the proposed methodology and its scalability allow to apply it to different contexts and at a different administrative level, from the urban scale to a national one. Moreover, the model is able to provide a decision-support tool for the design of strategic plans to contrast pandemics based on respiratory diseases.

An observational study of the Covid-19 situation following the first pan-European mass sports event

BACKGROUND

All events in Europe, including EURO2020 football championship, were postponed due to Covid-19 in 2020. Instead, Euro2020 took place in 2021, as mitigation measures were relaxed, cross-country mobility increased and the Delta variant was spreading across Europe. This study explored the possibility of an increased Covid-19 spread across Europe following EURO2020 matches.

METHODS

Covid-19 data on cases, vaccination and delta variant for participating countries, host cities/regions and neighbouring countries, for May till July 2021, were obtained from European Centre for Disease Prevention and Control, Our World in Data, Johns Hopkins COVID-19 Dashboard and the UK Government website. EURO2020 data were obtained from the Union of European Football Associations official website.

RESULTS

A general increase in Covid-19 positivity trend in Europe was observed following a week of EURO2020 matches across most countries and host cities. A similar trend was observed for the Delta variant sample positivity rate. The increased incidence was mostly among the young generation (<49 years). A decline in positive cases was observed on a national level for most countries following the Finals match.

CONCLUSION

The EURO2020 was an anticipated mass sports event, and it was the first-time spectators were allowed to enter stadiums in Europe. Stadiums instituted several mitigations to safeguard the spectators although reports of transmission were still present. The major challenges were the gatherings outside the stadiums that might have contributed to these observations. Targeted restrictions might be required during mass sport events especially in the presence of highly transmissible variant(s) and low vaccination rates among the young generation.

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease's spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.

Genome Evolution and Early Introductions of the SARS-CoV-2 Omicron Variant in Mexico

A new variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), named Omicron (Pango lineage designation B.1.1.529), was first reported to the World Health Organization by South African health authorities on 24 November 2021. The Omicron variant possesses numerous mutations associated with increased transmissibility and immune escape properties. In November 2021, Mexican authorities reported Omicron's presence in the country. In this study, we infer the first introductory events of Omicron and the impact that human mobility has had on the spread of the virus. We also evaluated the adaptive evolutionary processes in Mexican SARS-CoV-2 genomes during the first month of the circulation of Omicron. We inferred 160 introduction events of Omicron in Mexico since its first detection in South Africa; subsequently, after the first introductions there was an evident increase in the prevalence of SARS-CoV-2 during January. This higher prevalence of the novel variant resulted in a peak of reported cases; on average 6 weeks after, a higher mobility trend was reported. During the peak of cases in the country from January to February 2022, the Omicron BA.1.1 sub-lineage dominated, followed by the BA.1 and BA.15 sub-lineages. Additionally, we identified the presence of diversifying natural selection in the genomes of Omicron and found six non-synonymous mutations in the receptor binding domain of the spike protein, all of them related to evasion of the immune response. In contrast, the other proteins in the genome are highly conserved; however, we identified homoplasic mutations in non-structural proteins, indicating a parallel evolution.

On the use of aggregated human mobility data to estimate the reproduction number

The reproduction number of an infectious disease, such as CoViD-19, can be described through a modified version of the susceptible-infected-recovered (SIR) model with time-dependent contact rate, where mobility data are used as proxy of average movement trends and interpersonal distances. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 in terms of aggregated individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.