The Morbidity Analysis of 17 Notifiable Bacterial Infectious Diseases in China, 2004-2019

Background To better understand the epidemic dynamic of notifiable BIDs in China, and to provide scientific evidence for the prevention and control measures. Methods we gathered data from the NIDRIS in China from 2004 to 2019. The methods of descriptive epidemiology were applied to analyze the data of BIDs. The Joinpoint Regression analysis was utilized to examine trends in the incidence rates of BIDs. Results During 2004‒2019, the average annual incidence rate reported for notifiable BIDs was 134.00 of 100 000. The overall average annual percent changes (AAPC) for RTDs and DCFTDs was -1.98% and -11.66%, respectively. Both of BSTDs and ZVDs showed increasing trends with AAPCs of 4.74% and 4.46%, respectively. Pertussis and scarlet fever showed the fastest increase of the incidence rate in the age group of 0~5 years with AAPC of 15.17% and 12.05%. RTDs had the highest incidence rate in Northwest China. South and East China represented a higher morbidity in BSTDs. The proportion of laboratory confirmation of BIDs have increased from 43.80–64.04%. Conclusions RTDs and DCFTDs showed an overall downward trend in China for a dozen years, while BSTDs and ZVDs indicated significantly upward trends.

Application of Big Data Technology in the Fight Against New Coronary Pneumonia Epidemic in China

In the fight against New Coronary Pneumonia Epidemic, Chinese Ministry of Health put forward the inevitable requirements of precise policy implementation and scientific epidemic prevention. Accordingly, the big data technology has been applied in the analysis of epidemic dynamic, information inquiry, disease prevention and treatment, and prediction of epidemic trend. And, great success has been achieved in the fight, where the big data technology has played a vital role. This article outlines the main applications of big data technology in the prevention and control of New Coronary Pneumonia Epidemic, and proposes suggestions based on the problems in the application of big data during the epidemic prevention and control period. In the later stage, the integration of big data technology in various fields should be accelerated, information should be further shared and the utility value of data should be maximized.

Where to cut to delay a pandemic with minimum disruption? mathematical analysis based on the SIS model

We consider the problem of modifying a network topology in such a way as to delay the propagation of a disease with minimal disruption of the network capacity to reroute goods/items/passengers. We find an approximate solution to the Susceptible-Infected-Susceptible (SIS) model, which constitutes an upper bound to its exact solution. This upper bound allows direct structure-epidemic dynamic relations via the total communicability function. Using this approach we propose a strategy to remove edges in a network that significantly delays the propagation of a disease across the network with minimal disruption of its capacity to deliver goods/items/passengers. We apply this strategy to the analysis of the UK airport transportation network weighted by the number of passengers transported in 2003. We find that the removal of all flights connecting four origin-destination pairs in the UK delays the propagation of a disease by more than 300%, with a minimal deterioration of the transportation capacity of this network. These time delays in the propagation of a disease represent an important non-pharmaceutical intervention to confront an epidemic, allowing for better preparations of the health systems, while keeping the economy moving with minimal disruptions.

The effect of population size for pathogen transmission on prediction of COVID-19 spread

Extreme public health interventions play a critical role in mitigating the local and global prevalence and pandemic potential. Here, we use population size for pathogen transmission to measure the intensity of public health interventions, which is a key characteristic variable for nowcasting and forecasting of COVID-19. By formulating a hidden Markov dynamic system and using nonlinear filtering theory, we have developed a stochastic epidemic dynamic model under public health interventions. The model parameters and states are estimated in time from internationally available public data by combining an unscented filter and an interacting multiple model filter. Moreover, we consider the computability of the population size and provide its selection criterion. With applications to COVID-19, we estimate the mean of the effective reproductive number of China and the rest of the globe except China (GEC) to be 2.4626 (95% CI: 2.4142–2.5111) and 3.0979 (95% CI: 3.0968–3.0990), respectively. The prediction results show the effectiveness of the stochastic epidemic dynamic model with nonlinear filtering. The hidden Markov dynamic system with nonlinear filtering can be used to make analysis, nowcasting and forecasting for other contagious diseases in the future since it helps to understand the mechanism of disease transmission and to estimate the population size for pathogen transmission and the number of hidden infections, which is a valid tool for decision-making by policy makers for epidemic control.

Information Agenda on Entertainment Radio Stations during the COVID19 Pandemic on the Example of “Radio Dacha”

The coronavirus pandemic has had a significant impact on the Russian media, which, regardless of their format, have been broadcasting news about the infection since the beginning of the outbreak on a regular basis. The main purpose of the research is to establish a connection between the epidemiological situation in Russia during the first wave and the nature of the change in the information agenda on the air of the entertainment “Radio Dacha”. To reach this goal the author, using the method of inclusive observation, analyzes 1 219 episodes of the news program, aired from January 2020, when radio hosts first mentioned the COVID-19, to July 2020, when the main restrictions were lifted in Russia. The article shows that depending on the epidemiological state, the number of notes about the coronavirus increased. The maximum quantity of news about the disease was noted in April and May when the country had the highest amount of cases and announced a lockdown. Meanwhile, the content analysis indicated that there was direct and indirect news coverage of the coronavirus. Their ratio is 97 to 3 % in favor of direct news. It means that despite the format of the radio station, the radio hosts only talked about political and social news with little or no entertain ment content. Thus, the epidemic dynamic changed the information agenda, and the worst it was, the more news about the coronavirus went on the air. The news about the infection became the longest discussed subject on the air of “Radio Dacha”.

Modeling and reviewing analysis of the COVID-19 epidemic in Algeria with diagnostic shadow

In this paper, we formulate a special epidemic dynamic model to describe the transmission of COVID-19 in Algeria. We derive the threshold parameter control reproduction number (R0c ), and present the effective control reproduction number (Rc(t)) as a real-time index for evaluating the epidemic under different control strategies. Due to the limitation of the reported data, we redefine the number of accumulative confirmed cases with diagnostic shadow and then use the processed data to do the optimal numerical simulations. According to the control measures, we divide the whole research period into six stages. And then the corresponding medical resource estimations and the average effective control reproduction numbers for each stage are given. Meanwhile, we use the parameter values which are obtained from the optimal numerical simulations to forecast the whole epidemic tendency under different control strategies.

A snapshot of a pandemic: the interplay between social isolation and COVID-19 dynamics in Brazil

SummaryIn response to the COVID-19 pandemic, most governments around the world implemented some kind of social distancing policy in an attempt to block the spreading of the virus within a territory. In Brazil, this mitigation strategy was first implemented in March 2020 and mainly monitored by social isolation indicators built from mobile geolocation data. While it is well known that social isolation has been playing a crucial role in epidemic control, the precise connections between mobility data indicators and epidemic dynamic parameters have a complex interdependence. In this work, we investigate this dependence for several Brazilian cities, looking also at socioeconomic and demographic factors that influence it. As expected, the increase in the social isolation indicator was shown to be related to the decrease in the speed of transmission of the disease, but the relation was shown to depend on the urban hierarchy level of the city, the human development index and also the epidemic curve stage. Moreover, a high social isolation at the beginning of the epidemic relates to a strong positive impact on flattening the epidemic curve, while less efficacy of this mitigation strategy was observed when it has been implemented later. Mobility data plays an important role in epidemiological modeling and decision-making, however, we discuss in this work how a direct relationship between social isolation data and COVID-19 data is hard to be established. Understanding this interplay is a key factor to better modeling, for which we hope this study contributes.

Evolutionary Dynamics of Stochastic SEIR Models with Migration and Human Awareness in Complex Networks

In this paper, a stochastic SEIR (Susceptible-Exposed-Infected-Removed) epidemic dynamic model with migration and human awareness in complex networks is constructed. The awareness is described by an exponential function. The existence of global positive solutions for the stochastic system in complex networks is obtained. The sufficient conditions are presented for the extinction and persistence of the disease. Under the conditions of disease persistence, the distance between the stochastic solution and the local disease equilibrium of the corresponding deterministic system is estimated in the time sense. Some numerical experiments are also presented to illustrate the theoretical results. Although the awareness introduced in the model cannot affect the extinction of the disease, the scale of the disease will eventually decrease as human awareness increases.