Urban diversity and epidemic resilience: The case of the COVID-19.
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ABSTRACT:
SUBMITTER: Hananel R
PROVIDER: S-EPMC8660207 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:By employing the city-level data from China during the spring of 2020, this study investigates the relationship between city-level resilience against the outbreak of COVID-19 pandemics and its affecting factors, including the inflow risk pressure of COVID-19 virus (population inflow from the epicenter), city agglomeration characteristics (urban population density and city size), healthcare resource adequacy, among others. The results reveal that, while managing COVID-19 inflow risk pressure plays a critical role in the city's pandemic disaster resilience. city agglomeration characteristics also matters. To be exact, we find that large and high-density cities with high inter and intra-city mobility flows have more difficulties in containing the epidemic spread, but improving healthcare infrastructure adequacy and urban governance capacity can increase time efficacy of pandemic control and then improve the city's resilience against pandemic. Although our analysis is based on the performance of Chinese cities in the case of COVID-19, the research framework can be applied in understanding COVID-19 control performance of cities in other countries and the findings can be useful for improving health-related urban resilience and sustainability.
| S-EPMC8008811 | biostudies-literature
Project description:Multiple small- to middle-scale cities, mostly located in northern China, became epidemic hotspots during the second wave of the spread of COVID-19 in early 2021. Despite qualitative discussions of potential social-economic causes, it remains unclear how this unordinary pattern could be substantiated with quantitative explanations. Through the development of an urban epidemic hazard index (EpiRank) for Chinese prefectural districts, we came up with a mathematical explanation for this phenomenon. The index is constructed via epidemic simulations on a multi-layer transportation network interconnecting local SEIR transmission dynamics, which characterizes intra- and inter-city population flow with a granular mathematical description. Essentially, we argue that these highlighted small towns possess greater epidemic hazards due to the combined effect of large local population and small inter-city transportation. The ratio of total population to population outflow could serve as an alternative city-specific indicator of such hazards, but its effectiveness is not as good as EpiRank, where contributions from other cities in determining a specific city's epidemic hazard are captured via the network approach. Population alone and city GDP are not valid signals for this indication. The proposed index is applicable to different epidemic settings and can be useful for the risk assessment and response planning of urban epidemic hazards in China. The model framework is modularized and the analysis can be extended to other nations.
| S-EPMC8290012 | biostudies-literature
Project description:The COVID-19 pandemic is one of the most pressing issues at present. A question which is particularly important for governments and policy makers is the following: Does the virus spread in the same way in different countries? Or are there significant differences in the development of the epidemic? In this paper, we devise new inference methods that allow to detect differences in the development of the COVID-19 epidemic across countries in a statistically rigorous way. In our empirical study, we use the methods to compare the outbreak patterns of the epidemic in a number of European countries.
| S-EPMC8141781 | biostudies-literature
Project description:Background Due to the continual recurrence of COVID-19 in urban areas, it is important to know more about the evolution of the epidemic within this setting to mitigate the risk of the situation getting worse. As the virus spreads through human society, the social networks of confirmed cases can provide us with crucial new insights on this question. Methods Based on the epidemiological reports of 235 COVID-19 cases in Nanjing, we constructed a social contact network for the epidemic. By analyzing the structure of this network, we explored the transmission characteristics of the epidemic, to provide evidence-based explanations for its transmission. Results In our constructed transmission network, more than half (95/165, 57.58%) of patients were found not to have transmitted the infection, with only 15 (9.10%) source patients accounting for more than a third of the contagion (60, 36.36%), suggesting that the transmission of COVID-19 varies per individuals. Patients in the 31 to 50 age group were the main source of infectious clusters, with females playing a more active role in passing on the infection. Network component analysis identified nine components with disproportionate concentrations of influential patients, accounting for 49.09% (81) of the patients and 59.09% (78) of epidemiological network contacts. Family aggregation may favor disease transmission, and parenthood is the relationship with the highest infection risk within the family cluster. In addition, some specific public places, such as chess and card parlors, were found to be notable hotspots for community infection. Conclusion This study presents the evolution of the urban epidemic from the perspective of individual-level and socially interactive processes. This real-world evidence can help to increase public awareness of the epidemic, formulate countermeasures, and allocate limited public health resources for urban management.
| S-EPMC9195001 | biostudies-literature
Project description:PurposeThere is a growing concern about the COVID-19 epidemic intensifying in rural areas in the United States (U.S.). In this study, we described the dynamics of COVID-19 cases and deaths in rural and urban counties in the U.S.MethodsUsing data from April 1 to November 12, 2020, from Johns Hopkins University, we estimated COVID-19 incidence and mortality rates and conducted comparisons between urban and rural areas in three time periods at the national level, and in states with higher and lower COVID-19 incidence rates.ResultsResults at the national level showed greater COVID-19 incidence rates in urban compared to rural counties in the Northeast and Mid-Atlantic regions of the U.S. at the beginning of the epidemic. However, the intensity of the epidemic has shifted to a rapid surge in rural areas. In particular, high incidence states located in the Mid-west of the country had more than 3,400 COVID-19 cases per 100,000 people compared to 1,284 cases per 100,000 people in urban counties nationwide during the third period (August 30 to November 12).ConclusionsOverall, the current epicenter of the epidemic is located in states with higher infection rates and mortality in rural areas. Infection prevention and control efforts including healthcare capacity should be scaled up in these vulnerable rural areas.
| S-EPMC8061094 | biostudies-literature
Project description:COVID-19, also known as SARS-CoV-2, is a coronavirus that is highly pathogenic and virulent. It spreads very quickly through close contact, and so in response to growing numbers of cases, many countries have imposed lockdown measures to slow its spread around the globe. The purpose of a lockdown is to reduce reproduction, that is, the number of people each confirmed case infects. Lockdown measures have worked to varying extents but they come with a massive price. Nearly every individual, community, business, and economy has been affected. In this paper, switching strategies that take into account the total "cost" borne by a community in response to COVID-19 are proposed. The proposed cost function takes into account the health and well-being of the population, as well as the economic impact due to the lockdown. The model allows for a comparative study to investigate the effectiveness of various COVID-19 suppression strategies. It reveals that both the strategy to implement a lockdown and the strategy to maintain an open community are individually losing in terms of the total "cost" per day. However, switching between these two strategies in a certain manner can paradoxically lead to a winning outcome-a phenomenon attributed to Parrondo's paradox.
| S-EPMC7740105 | biostudies-literature
Project description:BackgroundSince the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that drive its local transmission cycles to make better decisions regarding prevention and control measures. Different modeling approaches have been proposed in an attempt to predict the behavior of these local cycles.ObjectiveThis paper presents a framework to characterize the different variables that drive the local, or epidemic, cycles of the COVID-19 pandemic, in order to provide a set of relatively simple, yet efficient, statistical tools to be used by local health authorities to support decision making.MethodsVirtually closed cycles were compared to cycles in progress from different locations that present similar patterns in the figures that describe them. With the aim to compare populations of different sizes at different periods of time and locations, the cycles were normalized, allowing an analysis based on the core behavior of the numerical series. A model for the reproduction number was derived from the experimental data, and its performance was presented, including the effect of subnotification (ie, underreporting). A variation of the logistic model was used together with an innovative inventory model to calculate the actual number of infected persons, analyze the incubation period, and determine the actual onset of local epidemic cycles.ResultsThe similarities among cycles were demonstrated. A pattern between the cycles studied, which took on a triangular shape, was identified and used to make predictions about the duration of future cycles. Analyses on effective reproduction number (Rt) and subnotification effects for Germany, Italy, and Sweden were presented to show the performance of the framework introduced here. After comparing data from the three countries, it was possible to determine the probable dates of the actual onset of the epidemic cycles for each country, the typical duration of the incubation period for the disease, and the total number of infected persons during each cycle. In general terms, a probable average incubation time of 5 days was found, and the method used here was able to estimate the end of the cycles up to 34 days in advance, while demonstrating that the impact of the subnotification level (ie, error) on the effective reproduction number was <5%.ConclusionsIt was demonstrated that, with relatively simple mathematical tools, it is possible to obtain a reliable understanding of the behavior of COVID-19 local epidemic cycles, by introducing an integrated framework for identifying cycle patterns and calculating the variables that drive it, namely: the Rt, the subnotification effects on estimations, the most probable actual cycles start dates, the total number of infected, and the most likely incubation period for SARS-CoV-2.
| S-EPMC8078446 | biostudies-literature
Project description:ObjectivesMexico is among the countries in Latin America hit hardest by coronavirus disease 2019 (COVID-19). A large proportion of older adults in Mexico have high prevalence of multimorbidity and live in poverty with limited access to health care services. These statistics are even higher among adults living in rural areas, which suggest that older adults in rural communities may be more susceptible to COVID-19. The objectives of the article were to compare clinical and demographic characteristics for people diagnosed with COVID-19 by age group, and to describe cases and mortality in rural and urban communities.MethodWe linked publicly available data from the Mexican Ministry of Health and the Census. Municipalities were classified based on population as rural (<2,500), semirural (≥2,500 and <15,000), semiurban (≥15,000 and <100,000), and urban (≥100,000). Zero-inflated negative binomial models were performed to calculate the total number of COVID-19 cases, and deaths per 1,000,000 persons using the population of each municipality as a denominator.ResultsOlder adults were more likely to be hospitalized and reported severe cases, with higher mortality rates. In addition, rural municipalities reported a higher number of COVID-19 cases and mortality related to COVID-19 per million than urban municipalities. The adjusted absolute difference in COVID-19 cases was 912.7 per million (95% confidence interval [CI]: 79.0-1746.4) and mortality related to COVID-19 was 390.6 per million (95% CI: 204.5-576.7).DiscussionUrgent policy efforts are needed to mandate the use of face masks, encourage handwashing, and improve specialty care for Mexicans in rural areas.
| S-EPMC7798580 | biostudies-literature
Project description:Assessing and managing the impact of large-scale epidemics considering only the individual risk and severity of the disease is exceedingly difficult and could be extremely expensive. Economic consequences, infrastructure and service disruption, as well as the recovery speed, are just a few of the many dimensions along which to quantify the effect of an epidemic on society's fabric. Here, we extend the concept of resilience to characterize epidemics in structured populations, by defining the system-wide critical functionality that combines an individual's risk of getting the disease (disease attack rate) and the disruption to the system's functionality (human mobility deterioration). By studying both conceptual and data-driven models, we show that the integrated consideration of individual risks and societal disruptions under resilience assessment framework provides an insightful picture of how an epidemic might impact society. In particular, containment interventions intended for a straightforward reduction of the risk may have net negative impact on the system by slowing down the recovery of basic societal functions. The presented study operationalizes the resilience framework, providing a more nuanced and comprehensive approach for optimizing containment schemes and mitigation policies in the case of epidemic outbreaks.
| S-EPMC5789872 | biostudies-other
Project description:During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.
| S-EPMC9931295 | biostudies-literature