Applications of the Variance of Final Outbreak Size for Disease Spreading in Networks

2013 ◽  
Vol 16 (4) ◽  
pp. 839-862
Author(s):  
Lilia L. Ramírez-Ramírez ◽  
Mary E. Thompson
Keyword(s):  
Disease Spreading ◽  
Outbreak Size

scholarly journals Nowcasting and Forecasting the Spread of COVID-19 in Iran

2020 ◽  
Author(s):  
Hamidreza Masjedi ◽  
Jomar F. Rabajante ◽  
Fatemeh Bahranizadd ◽  
Mohammad Hosein Zare
Keyword(s):  
Decision Making ◽  
Phenomenological Models ◽  
Cumulative Incidence ◽  
Mechanistic Model ◽  
Control Measures ◽  
Seir Model ◽  
The World ◽  
Disease Spreading ◽  
The Government ◽  
Outbreak Size

AbstractIntroductionAs of early December 2019, COVID-19, a disease induced by SARS-COV-2, has started spreading, originated in Wuhan, China, and now on, have infected more than 2 million individuals throughout the world.PurposeThis study aimed to nowcast the COVID-19 outbreak throughout Iran and to forecast the trends of the disease spreading in the upcoming month.MethodsThe cumulative incidence and fatality data were extracted from official reports of the National Ministry of Health and Medical Educations of Iran. To formulate the outbreak dynamics, six phenomenological models, as well as a modified mechanistic Susciptible-Exposed-Infectious-Recovered (SEIR) model, were implemented. The models were calibrated with the integrated data, and trends of the epidemic in Iran was then forecasted for the next month.ResultsThe final outbreak size calculated by the best fitted phenomenological models was estimated to be in the range of 68,486 to 118,923 cases; however, the calibrated SEIR model estimated that the outbreak would rage again, starting from April 26. Moreover, projected by the mechanistic model, approximately half of the infections have undergone undetected.ConclusionAlthough the advanced phenomenological models perfectly fitted the data, they are incapable of applying behavioral aspects of the outbreak and hence, are not reliable enough for authorities’ decision adoptions. In contrast, the mechanistic SEIR model alarms that the COVID-19 outbreak in Iran may peak for the second time, consequent to lifting the control measures. This implies that the government may implement a more granular decision making to control the outbreak.

COVID-19: A comparative study of SARS, MERS and COVID-19

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Chandra Mohan ◽  
Vinod Kumar
Keyword(s):  
Public Health ◽  
Comparative Analysis ◽  
Central China ◽  
World Health ◽  
Unknown Aetiology ◽  
Economic Sectors ◽  
Past Life ◽  
Current Article ◽  
Disease Spreading ◽  
Health Organization

: World Health Organization (WHO) office in China received the information of pneumonia cases of unknown aetiology from Wuhan, central China on 31st December 2019, subsequently this disease spreading in china and rest of world. Till the March 2020 end, more than 2 lakhs confirmed cases with more than 70000 deaths were reported worldwide, very soon researchers identified it as novel beta Corona virus (virus SARS-CoV-2) and its infection coined as COVID-19. Health ministries of various countries and WHO together fighting to this health emergency, which not only affects public health, but also started affecting various economic sectors as well. The main aim of the current article is to explore the various pandemic situations (SARS, MERS) in past, life cycle of COVID-19, diagnosis procedures, prevention and comparative analysis of COVID-19 with other epidemic situations.

scholarly journals An adaptive social distancing SIR model for COVID-19 disease spreading and forecasting

2021 ◽  
Vol 10 (s1) ◽  
Author(s):  
Said Gounane ◽  
Yassir Barkouch ◽  
Abdelghafour Atlas ◽  
Mostafa Bendahmane ◽  
Fahd Karami ◽  
...  
Keyword(s):  
Mathematical Theory ◽  
Real Data ◽  
Sir Model ◽  
Epidemic Threshold ◽  
Social Distancing ◽  
Sir Epidemic ◽  
Proposed Model ◽  
Disease Spreading ◽  
The Government ◽  
The Impact

Abstract Recently, various mathematical models have been proposed to model COVID-19 outbreak. These models are an effective tool to study the mechanisms of coronavirus spreading and to predict the future course of COVID-19 disease. They are also used to evaluate strategies to control this pandemic. Generally, SIR compartmental models are appropriate for understanding and predicting the dynamics of infectious diseases like COVID-19. The classical SIR model is initially introduced by Kermack and McKendrick (cf. (Anderson, R. M. 1991. “Discussion: the Kermack–McKendrick Epidemic Threshold Theorem.” Bulletin of Mathematical Biology 53 (1): 3–32; Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of Epidemics.” Proceedings of the Royal Society 115 (772): 700–21)) to describe the evolution of the susceptible, infected and recovered compartment. Focused on the impact of public policies designed to contain this pandemic, we develop a new nonlinear SIR epidemic problem modeling the spreading of coronavirus under the effect of a social distancing induced by the government measures to stop coronavirus spreading. To find the parameters adopted for each country (for e.g. Germany, Spain, Italy, France, Algeria and Morocco) we fit the proposed model with respect to the actual real data. We also evaluate the government measures in each country with respect to the evolution of the pandemic. Our numerical simulations can be used to provide an effective tool for predicting the spread of the disease.

Predator-dependent transmissible disease spreading in prey under Holling type-II functional response

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Dipankar Ghosh ◽  
Prasun K. Santra ◽  
Abdelalim A. Elsadany ◽  
Ghanshaym S. Mahapatra
Keyword(s):  
Functional Response ◽  
Prey Species ◽  
Linearization Method ◽  
Logistic Growth ◽  
Type Ii ◽  
Disease Spreading ◽  
Predator Interactions ◽  
The Stability ◽  
Infected Prey ◽  
Type Ii Functional Response

Abstract This paper focusses on developing two species, where only prey species suffers by a contagious disease. We consider the logistic growth rate of the prey population. The interaction between susceptible prey and infected prey with predator is presumed to be ruled by Holling type II and I functional response, respectively. A healthy prey is infected when it comes in direct contact with infected prey, and we also assume that predator-dependent disease spreads within the system. This research reveals that the transmission of this predator-dependent disease can have critical repercussions for the shaping of prey–predator interactions. The solution of the model is examined in relation to survival, uniqueness and boundedness. The positivity, feasibility and the stability conditions of the fixed points of the system are analysed by applying the linearization method and the Jacobian matrix method.

scholarly journals Bounding the Size and Probability of Epidemics on Networks

2008 ◽  
Vol 45 (2) ◽  
pp. 498-512 ◽  
Author(s):  
Joel C. Miller
Keyword(s):  
Infectious Disease ◽  
Lower Bounds ◽  
Upper Bounds ◽  
Marginal Probability ◽  
Transmission Properties ◽  
Disease Spreading ◽  
Special Case

We consider an infectious disease spreading along the edges of a network which may have significant clustering. The individuals in the population have heterogeneous infectiousness and/or susceptibility. We define the out-transmissibility of a node to be the marginal probability that it would infect a randomly chosen neighbor given its infectiousness and the distribution of susceptibility. For a given distribution of out-transmissibility, we find the conditions which give the upper (or lower) bounds on the size and probability of an epidemic, under weak assumptions on the transmission properties, but very general assumptions on the network. We find similar bounds for a given distribution of in-transmissibility (the marginal probability of being infected by a neighbor). We also find conditions giving global upper bounds on the size and probability. The distributions leading to these bounds are network independent. In the special case of networks with high girth (locally tree-like), we are able to prove stronger results. In general, the probability and size of epidemics are maximal when the population is homogeneous and minimal when the variance of in- or out-transmissibility is maximal.

scholarly journals Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0241171
Author(s):  
Choujun Zhan ◽  
Chi K. Tse ◽  
Yuxia Fu ◽  
Zhikang Lai ◽  
Haijun Zhang
Keyword(s):  
Human Migration ◽  
Modeling And Prediction ◽  
Migration Data ◽  
Disease Spreading

scholarly journals Differences in power-law growth over time and indicators of COVID-19 pandemic progression worldwide

2020 ◽  
Author(s):  
Jack Merrin
Keyword(s):  
Error Analysis ◽  
Real Time ◽  
Power Law ◽  
Growth Phase ◽  
Exponential Growth ◽  
Spreading Rate ◽  
Exponential Growth Phase ◽  
Power Law Exponent ◽  
Disease Spreading ◽  
Over Time

1AbstractAn automated statistical and error analysis of 45 countries or regions with more than 1000 cases of COVID-19 as of March 28, 2020, has been performed. This study reveals differences in the rate of disease spreading rate over time in different countries. This survey observes that most countries undergo a beginning exponential growth phase, which transitions into a power-law phase, as recently suggested by Ziff and Ziff. Tracking indicators of growth, such as the power-law exponent, are a good indication of the relative danger different countries are in and show when social measures are effective towards slowing the spread. The data compiled here are usefully synthesizing a global picture, identifying country to country variation in spreading, and identifying countries most at risk. This analysis may factor into how best to track the effectiveness of social distancing policies and quarantines in real-time as data is updated each day.

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