Joint decisions for hospital admissions and horizontal medical resource transfer against capacity shortage in the early stage of pandemics.

For COVID-19, infected individuals are isolated from the community when they become symptomatic, and when they become recovered individuals who have immunity, they return to the community. With an increase in the number of recovered individuals in the community, the contact rate between infected individuals and susceptible individuals decreases, resulting in a decrease in the number of infected individuals. Vaccination causes not only a decrease in the number of susceptible individuals in the community but also a reduction in the contact rate between infected individuals and susceptible individuals with an increase in the number of vaccinated individuals, as does the number of recovered individuals. Namely, the total number of vaccinated individuals strongly controls the spread of COVID-19. From the viewpoint of herd immunity, the vaccination duration, which is the period between the start and the end of mass vaccination, must be examined because the number of vaccinated individuals actually increases gradually daily according to vaccination programs, and the total number of vaccinated individuals depends on the duration of mass vaccination. How long should we continue mass vaccination to contain the COVID-19 pandemic from a physical viewpoint? The number of individuals infected according to different durations of mass vaccination was calculated by a flexible compartment model specific to COVID-19. The model contains the vaccination rate as an independent variable in the calculation equations and, as the dependent variable, the number of isolated/ recovered individuals and the population excluding the individuals kept in isolation, both of which affect the contact rate between infected individuals and susceptible individuals. The effect of the duration of mass vaccination can be evaluated by comparing the total number of infected individuals among those calculated according to different durations of mass vaccination in the cases with different start dates of vaccination and/or with different vaccination rates and/or with different symptomatic rates. The results show that since the contact rate decreases with an increase in the number of vaccinated individuals, the earlier the start date of vaccination is, the smaller the total number of infected individuals becomes, and the longer the mass vaccination duration is, the smaller the total number of infected individuals becomes. The results also showed that when vaccination is continued until the day when the sum of the number of recovered individuals and the number of vaccinated individuals exceeds an ‘expedient herd immunity threshold’, the total number of infected individuals is significantly reduced, and the duration of infection is also markedly shortened, as expected in the case with a sufficient duration of mass vaccination. Keywords Asymptomatic; Compartment model; COVID-19; Duration of infection; Herd immunity; Infected; Infection during the latent period; Isolation; SIR model; Recovered; Susceptible; Symptomatic rate; Mass vaccination duration

Epidemic progression and vaccination in a heterogeneous population. Application to the Covid-19 epidemic

New “Severe Acute Respiratory Syndrome Coronavirus 2” (SARS-CoV-2) is responsible for a clinical entity named Coronavirus Disease 2109 (COVID-19) by the World Health Orgazination, which is now a pandemic (1). In Europe, Italy was the first, and so far the largest, epicenter of the epidemic. It is now clear that many asymptomatic and paucisymptomatic patients went undetected, causing significant diffusion of the disease. Asymptomatic infections are common: among 634 confirmed patients on board the “Diamond Princess” cruise ship, 306 were symptomatic and 328 asymptomatic at the time of testing (2). It is now clear that when a patient with atypical severe pneumonia was diagnosed as COVID-19 in a small city, Codogno, in a previously healthy 38-year-old man, on February 20, 2020, more patients with undetected COVID-19 were present in Northern Italy. In addition to the cluster around Codogno, about 50 km south of Milan, which was promptly put under strict lockdown, two additional relevant clusters emerged near the cities of Bergamo and Brescia. The delay in enforcing a lockdown in the latter areas is probably one of the leading causes of the more severe outbreak now affecting these provinces. Extensive testing in the initial phase of disease diffusion was adopted in the Italian Veneto area, resulting in better containment of new cases. A nationwide lockdown measure was passed by the Italian government when Northern Italy, which has the highest concentration of well organized hospitals in the country, started showing signs of difficulty handling the rapid increase of patients in need of treatment in intensive care units (ICUs). A comparison of Chinese and Italian data on COVID-19 may be of interest to other countries where the disease is now spreading. The Chinese Center for Disease Control and Prevention described the characteristics of a large cohort of COVID-19 patients (3), showing that …

South-south cooperation for Chagas disease

At the end of 2019, a new coronavirus (COVID-19) epidemic has triggered global public health concern. Here, a model integrating the daily intercity migration network, which constructed from real-world migration records and the Susceptible–Exposed–Infected–Removed model, is utilized to predict the epidemic spreading of the COVID-19 in more than 300 cities in China. However, the model has more than 1800 unknown parameters, which is a challenging task to estimate all unknown parameters from historical data within a reasonable computation time. In this article, we proposed a pseudocoevolutionary simulated annealing (SA) algorithm for identifying these unknown parameters. The large volume of unknown parameters of this model is optimized through three procedures co-adapted SA-based optimization processes, respectively. Our results confirm that the proposed method is both efficient and robust. Then, we use the identified model to predict the trends of the epidemic spreading of the COVID-19 in these cities. We find that the number of infections in most cities in China has reached their peak from February 29, 2020, to March 15, 2020. For most cities outside Hubei province, the total number of infected individuals would be less than 100, while for most cities in Hubei province (exclude Wuhan), the total number of infected individuals would be less than 3000.

A goal of many epidemic models is to compute the outcome of the epidemics from the observed infected early dynamics. However, often, the total number of infected individuals at the end of the epidemics is much lower than predicted from the early dynamics. This discrepancy is argued to result from human intervention or nonlinear dynamics not incorporated in standard models. We show that when variability in infection rates is included in standard susciptible-infected-susceptible () and susceptible-infected-recovered () models the total number of infected individuals in the late dynamics can be orders lower than predicted from the early dynamics. This discrepancy holds for and models, where the assumption that all individuals have the same sensitivity is eliminated. In contrast with network models, fixed partnerships are not assumed. We derive a moment closure scheme capturing the distribution of sensitivities. We find that the shape of the sensitivity distribution does not affect or the number of infected individuals in the early phases of the epidemics. However, a wide distribution of sensitivities reduces the total number of removed individuals in the model and the steady-state infected fraction in the model. The difference between the early and late dynamics implies that in order to extrapolate the expected effect of the epidemics from the initial phase of the epidemics, the rate of change in the average infectivity should be computed. These results are supported by a comparison of the theoretical model to the Ebola epidemics and by numerical simulation.

Control attenuation and temporary immunity in a cellular automata SEIR epidemic model

The Covid-19 pandemic created a significant challenge for public health agencies worldwide. Many have turned to social media as a tool for pandemic-related information dissemination. However, research on the effectiveness of social media for pandemic communication is still limited. This study explores social media communication by health agencies during the early stages of the pandemic in four countries which differ in their pandemic dynamics, cultural and geopolitical factors: the Netherlands, Poland, South Korea, and the United States. We focus on Twitter and identify themes present in the health agencies’ communication, as well as their relationship with the publics’ engagement. We find that the health agencies’ communication was mostly neutral and focused on providing sources for more information about the virus and basic information about the virus, however, there are differences between the countries.

Stability and threshold analysis of a class of epidemic models in a multi-patch environment.

The ongoing coronavirus disease 2019 (COVID-19) pandemic spread throughout China and worldwide since it was reported in Wuhan city, China in December 2019. 4 589 526 confirmed cases have been caused by the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), by May 18, 2020. At the early stage of the pandemic, the large-scale mobility of humans accelerated the spread of the pandemic. Rapidly and accurately tracking the population inflow from Wuhan and other cities in Hubei province is especially critical to assess the potential for sustained pandemic transmission in new areas. In this study, we first analyze the impact of related multisource urban data (such as local temperature, relative humidity, air quality, and inflow rate from Hubei province) on daily new confirmed cases at the early stage of the local pandemic transmission. The results show that the early trend of COVID-19 can be explained well by human mobility from Hubei province around the Chinese Lunar New Year. Different from the commonly-used pandemic models based on transmission dynamics, we propose a simple but effective short-term prediction model for COVID-19 cases, considering the human mobility from Hubei province to the target cities. The performance of our proposed model is validated by several major cities in Guangdong province. For cities like Shenzhen and Guangzhou with frequent population flow per day, the values of n}{}R^{2}n of daily prediction achieve 0.988 and 0.985. The proposed model has provided a reference for decision support of pandemic prevention and control in Shenzhen.

Dynamic behaviors and optimal control of a new delayed epidemic model

This paper is concerned with the use of vaccination schemes to control an epidemic in terms of the total number of individuals infected. In particular, monotonicity and continuity properties of total progeny of Crump-Mode-Jagers branching processes are derived depending on vaccination level. Furthermore, optimal vaccination policies based on the mean and quantiles of the total number of infected individuals are proposed. Finally, how to apply the proposed methodology in real situations is shown through a simulated example motivated by an outbreak of influenza virus in humans, in Indonesia.

BackgroundDuring 2021-2022, mainland China experienced multiple times of local COVID-19 outbreaks in several cities, including Yangzhou, Xi’an etc., and the Chinese government persistently adopted the zero-COVID policy in combating with the local outbreaks.MethodsWe develop a mathematical model with pulse population-wide nucleic acid screening, part of the zero-COVID policy, to reveal its role in controlling the spread of COVID-19. We calibrate the model by fitting the COVID-19 epidemic data of the local outbreaks in Yangzhou and Xi’an, China. Sensitivity analysis is conducted to investigate the impact of population-wide nucleic acid screening on controlling the outbreak of COVID-19.ResultsWithout the screening, the cumulative number of confirmed cases increases by 77.7% and 62.2% in Yangzhou and Xi’an, respectively. Meanwhile, the screening program helps to shorten the lockdown period for more than one month when we aim at controlling the cases into zero. Considering its role in mitigating the epidemics, we observe a paradox phenomenon of the screening rate in avoiding the runs on medical resource. That is, the screening will aggravate the runs on medical resource when the screening rate is small, while it helps to relieve the runs on medical resource if the screening rate is high enough. We also conclude that the screening has limited effects on mitigating the epidemics if the outbreak is in a high epidemic level or there has already been runs on medical resources. Alternatively, a smaller screening population per time with a higher screening frequency may be a better program to avoid the runs on medical resources.ConclusionsThe population-wide nucleic acid screening strategy plays an important role in quickly controlling and stopping the local outbreaks under the zero-COVID policy. However, it has limited impacts and even increase the potential risk of the runs on medical resource for containing the large scale outbreaks.

This article considers the minimization of the total number of infected individuals over the course of an epidemic in which the rate of infectious contacts can be reduced by time-dependent nonpharmaceutical interventions. The societal and economic costs of interventions are taken into account using a linear budget constraint which imposes a trade-off between short-term heavy interventions and long-term light interventions. We search for an optimal intervention strategy in an infinite-dimensional space of controls containing multiple consecutive lockdowns, gradually imposed and lifted restrictions, and various heuristic controls based for example on tracking the effective reproduction number. Mathematical analysis shows that among all such strategies, the global optimum is achieved by a single constant-level lockdown of maximum possible magnitude. Numerical simulations highlight the need of careful timing of such interventions, and illustrate their benefits and disadvantages compared to strategies designed for minimizing peak prevalence. Rather counterintuitively, adding restrictions prior to the start of a well-planned intervention strategy may even increase the total incidence.

Medical information systems (MIS) play a vital role in managing and scheduling medical resources to underpin healthcare services, which has become more critically important during major public health emergencies. During the Covid-19 pandemic, MIS is facing significant challenges to cope with the surge in demands of medical resources, resulting in more deaths and wider spreading of the disease. Our research examines how to allocate and utilize the medical resources across hospitals in a more accurate, and effective way to mitigate medical resource shortages and sustain the resource provisions. This paper mainly investigated the hospital’s supply-and-demand problems for medical resources under major public health emergencies by analyzing the allocation of medical staff resources. Furthermore, a formal method based on the Colored Petri Nets (CPN) has been proposed to model and characterize the medical business process and resource scheduling tasks. The experiments demonstrate that our approach can correctly and efficiently complete the dynamical scheduling process for surging requests.