scholarly journals A model for the spread of infectious diseases compatible with case data

2021 ◽  
Vol 477 (2254) ◽  
Author(s):  
Norden E. Huang ◽  
Fangli Qiao ◽  
Qian Wang ◽  
Hong Qian ◽  
Ka-Kit Tung
Keyword(s):  
Herd Immunity ◽  
Infection Model ◽  
First Principle ◽  
Straight Line ◽  
Model Predictions ◽  
Ill Posed ◽  
Deadly Disease ◽  
Using Data ◽  
Case Data ◽  
Instantaneous Recovery

For epidemics such as COVID-19, with a significant population having asymptomatic, untested infection, model predictions are often not compatible with data reported only for the cases confirmed by laboratory tests. Additionally, most compartmental models have instantaneous recovery from infection, contrary to observation. Tuning such models with observed data to obtain the unknown infection rate is an ill-posed problem. Here, we derive from the first principle an epidemiological model with delay between the newly infected ( N ) and recovered ( R ) populations. To overcome the challenge of incompatibility between model and case data, we solve for the ratios of the observed quantities and show that log( N ( t )/ R ( t )) should follow a straight line. This simple prediction tool is accurate in hindcasts verified using data for China and Italy. In traditional epidemiology, an epidemic wanes when much of the population is infected so that ‘herd immunity’ is achieved. For a highly contagious and deadly disease, herd immunity is not a feasible goal without human intervention or vaccines. Even before the availability of vaccines, the epidemic was suppressed with social measures in China and South Korea with much less than 5% of the population infected. Effects of social behaviour should be and are incorporated in our model.

scholarly journals Understanding Unreported Cases in the COVID-19 Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions

Biology ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 50 ◽  
Author(s):  
Zhihua Liu ◽  
Pierre Magal ◽  
Ousmane Seydi ◽  
Glenn Webb
Keyword(s):  
Public Health ◽  
Mathematical Model ◽  
Health Interventions ◽  
Epidemic Outbreak ◽  
Public Health Interventions ◽  
Model Predictions ◽  
Control And Prevention ◽  
Parameterized Model ◽  
Case Data ◽  
Municipal Health

We develop a mathematical model to provide epidemic predictions for the COVID-19 epidemic in Wuhan, China. We use reported case data up to 31 January 2020 from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the parameterized model, we identify the number of unreported cases. We then use the model to project the epidemic forward with varying levels of public health interventions. The model predictions emphasize the importance of major public health interventions in controlling COVID-19 epidemics.

Estimation of the Time-Dependent Profile of a Melting Front by Inverse Resolution

1997 ◽  
Vol 119 (3) ◽  
pp. 574-578 ◽  
Author(s):  
B. Guerrier ◽  
H. G. Liu ◽  
C. Be´nard
Keyword(s):  
Solid Phase ◽  
Transfer Problem ◽  
Numerical Study ◽  
A Priori ◽  
Inverse Operator ◽  
Melting Front ◽  
Inverse Heat Transfer ◽  
Ill Posed ◽  
Using Data ◽  
Inverse Resolution

The profile and time evolution of a solid/liquid interface in a phase change process is estimated by solving an inverse heat transfer problem, using data measurements in the solid phase only. One then faces the inverse resolution of a heat equation in a variable and a priori unknown 2D domain. This ill-posed problem is solved by a regularization approach: the unknown function (position of the melting front) is obtained by minimization of a two component criterion, consisting of a distance between the output of a simulation model and the measured data, to which a penalizing function is added in order to restore the continuity of the inverse operator. A numerical study is developed to analyze the validity domain of the identification method. From simulation tests, it is shown that the minimum signal/noise ratio that can be handled depends strongly on the position of the measurement sensors.

scholarly journals Prediction of Stem Rust Infection Favorability, by Means of Degree-Hour Wetness Duration, for Perennial Ryegrass Seed Crops

2003 ◽  
Vol 93 (4) ◽  
pp. 467-477 ◽  
Author(s):  
W. F. Pfender
Keyword(s):  
Perennial Ryegrass ◽  
Stem Rust ◽  
Infection Model ◽  
Data Sets ◽  
Infection Level ◽  
Time Intervals ◽  
Inoculum Level ◽  
Production Region ◽  
Using Data ◽  
Seed Crops

A weather-based infection model for stem rust of perennial ryegrass seed crops was developed and tested using data from inoculated bioassay plants in a field environment with monitored weather. The model describes favorability of daily weather as a proportion (0.0 to 1.0) of the maximum possible infection level set by host and inoculum. Moisture duration and temperature are combined in one factor as wet degree-hours (DHw) (i.e., degree-hours > 2.0°C summed only over time intervals when) moisture is present). Degree-hours are weighted as a function of temperature, based on observed rates of urediniospore germination. The pathogen Puccinia graminis subsp. graminicola requires favorable conditions of temperature and moisture during the night (dark period) and also at the beginning of the morning (light period), and both periods are included in the model. There is a correction factor for reduced favorability if the dark wet period is interrupted. The model is: proportion of maximum infection = 1 - e(-0.0031) × (DHw Index), where DHw Index is the product of interruption-adjusted overnight weighted DHw multiplied by morning (first 2 h after sunrise) weighted DHw. The model can be run easily with measurements from automated dataloggers that record temperature and wetness readings at frequent time intervals. In tests with three independent data sets, the model accounted for 80% of the variance in log(observed infection level) across three orders of magnitude, and the regression lines for predicted and observed values were not significantly different from log(observed) = log(predicted). A simpler version of the model using nonweighted degree hours (>2.0°C) was developed and tested. It performed nearly as well as the weighted-degree-hour model under conditions when temperatures from sunset to 2 h past sunrise were mostly between 4 and 20°C, as is the case during the growing season in the major U.S. production region for cool-season grass seed. The infection model is intended for use in combination with measured or modeled estimates of inoculum level, to derive estimates of daily infection.

scholarly journals Psychological characteristics and the mediating role of the 5C Model in explaining students’ COVID-19 vaccination intention

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255382
Author(s):  
Annelot Wismans ◽  
Roy Thurik ◽  
Rui Baptista ◽  
Marcus Dejardin ◽  
Frank Janssen ◽  
...  
Keyword(s):  
Perceived Risk ◽  
Herd Immunity ◽  
Collective Responsibility ◽  
Psychological Characteristics ◽  
Need To Belong ◽  
Mediation Analyses ◽  
Mediating Role ◽  
Health Authorities ◽  
The Government ◽  
Using Data

To achieve herd immunity against COVID-19, it is crucial to know the drivers of vaccination intention and, thereby, vaccination. As the determinants of vaccination differ across vaccines, target groups and contexts, we investigate COVID-19 vaccination intention using data from university students from three countries, the Netherlands, Belgium and Portugal. We investigate the psychological drivers of vaccination intention using the 5C model as mediator. This model includes five antecedents of vaccination: Confidence, Complacency, Constraints, Calculation and Collective Responsibility. First, we show that the majority of students have a positive propensity toward getting vaccinated against COVID-19, though only 41% of students are completely acceptant. Second, using the 5C model, we show that ‘Confidence’ (β = 0.33, SE = 03, p < .001) and ‘Collective Responsibility’ (β = 0.35, SE = 04, p < .001) are most strongly related to students’ COVID-19 vaccination intention. Using mediation analyses, we show that the perceived risk and effectiveness of the vaccine as well as trust in the government and health authorities indirectly relate to vaccination intention through ‘Confidence’. The perceived risk of COVID-19 for one’s social circle and altruism, the need to belong and psychopathy traits indirectly relate to vaccination intention through ‘Collective Responsibility’. Hence, targeting the psychological characteristics associated with ‘Confidence’ and ‘Collective Responsibility’ can improve the effectiveness of vaccination campaigns among students.

scholarly journals Analysis of COVID-19 and comorbidity co-infection Model with Optimal Control

2020 ◽  
Author(s):  
Andrew Omame ◽  
Ndolane Sene ◽  
Ikenna Nometa ◽  
Cosmas Ifeanyi Nwakanma ◽  
Emmanuel Ugochukwu Nwafor ◽  
...  
Keyword(s):  
Optimal Control ◽  
Detection Rate ◽  
Control Strategies ◽  
Cost Effective ◽  
Cerebrovascular Diseases ◽  
Infection Model ◽  
Infection Rates ◽  
Effective Contact ◽  
Using Data ◽  
Increased Susceptibility

The new coronavirus disease 2019 (COVID-19) infection is a double challenge for people infected with comorbidities such as cardiovascular and cerebrovascular diseases and diabetes. Comorbidities have been reported to be risk factors for the complications of COVID-19. In this work, we develop and analyze a mathematical model for the dynamics of COVID-19 infection in order to assess the impacts of prior comorbidity on COVID-19 complications and COVID-19 re-infection. The model is simulated using data relevant to the dynamics of the diseases in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID-19 infection by comorbid susceptibles as well as the rate of re-infection by those who have recovered from a previous COVID-19 infection. Sensitivity analysis of the model when the population of individuals co-infected with COVID-19 and comorbidity is used as response function revealed that the top ranked parameters that drive the dynamics of the co-infection model are the effective contact rate for COVID-19 transmission, βcv, the parameter accounting for increased susceptibility to COVID-19 by comorbid susceptibles, χcm, the comorbidity development rate, θcm, the detection rate for singly infected and co-infected individuals, η1 and η2, as well as the recovery rate from COVID-19 for co-infected individuals, ϕi2. Simulations of the model reveal that the cumulative confirmed cases (without comorbidity) may get up to 180,000 after 200 days, if the hyper susceptibility rate of comorbid susceptibles is as high as 1.2 per day. Also, the cumulative confirmed cases (including those co-infected with comorbidity) may be as high as 1000,000 cases by the end of November, 2020 if the re-infection rates for COVID-19 is 0.1 per day. It may be worse than this if the re-infection rates increase higher. Moreover, if policies are strictly put in place to step down the probability of COVID-19 infection by comorbid susceptibles to as low as 0.4 per day and step up the detection rate for singly infected individuals to 0.7 per day, then the reproduction number can be brought very low below one, and COVID-19 infection eliminated from the population. In addition, optimal control and cost-effectiveness analysis of the model reveal that the the strategy that prevents COVID-19 infection by comorbid susceptibles has the least ICER and is the most cost-effective of all the control strategies for the prevention of COVID-19.

scholarly journals ON THE UNCERTAINTY ABOUT HERD IMMUNITY LEVELS REQUIRED TO STOP COVID-19 EPIDEMICS

2020 ◽  
Author(s):  
Daniel Gianola
Keyword(s):  
Single Point ◽  
Herd Immunity ◽  
Point Estimate ◽  
Statistical Uncertainty ◽  
Full Characterization ◽  
Using Data

AbstractCOVID-19 evolved into a pandemic in 2020 affecting more than 150 countries. Given the absence of a vaccine, discussion has taken place on the strategy of allowing the virus to spread in a population, to increase population “herd immunity”. Knowledge of the minimum proportion of a population required to have recovered from COVID-19 infection in order to attain “herd” immunity, Pcrit, is important for formulating epidemiological policy. A method for measuring uncertainty about Pcrit based on a widely used package, EpiEstim, is derived. The procedure is illustrated using data from twelve countries at two early times during the COVID-19 epidemic. It is shown that simple plug-in measures of confidence on estimates of Pcrit are misleading, but that a full characterization of statistical uncertainty can be derived from EpiEstim, which reports percentiles only. Because of the important levels of uncertainty, it is risky to design epidemiological policy based on guidance provided by a single point estimate.

scholarly journals Modelling infectious diseases with herd immunity in a randomly mixed population

2021 ◽  
Author(s):  
Kian Boon Law ◽  
Kalaiarasu M. Peariasamy ◽  
Hishamshah Mohd. Ibrahim ◽  
Noor Hisham Abdullah
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Transmission Rate ◽  
Herd Immunity ◽  
Sir Model ◽  
Mixed Population ◽  
Transmission Dynamics ◽  
Transmission Model ◽  
Infection Model ◽  
Vaccination Rates

Abstract Background The conventional susceptible-infectious-recovered (SIR) model tends to overestimate the transmission dynamics of infectious diseases and ends up with total infections and total immunized population exceeding the threshold required for control and eradication of infectious diseases. The study aims to overcome the limitation by allowing the transmission rate of infectious disease to decline along with the reducing risk of contact infection. Methods Two new SIR models were developed to mimic the declining transmission rate of infectious diseases at different stages of transmission. Model A mimicked the declining transmission rate along with the reducing risk of transmission following infection, while Model B mimicked the declining transmission rate following recovery. Then, the conventional SIR model, Model A and Model B were used to simulate an infectious disease with a basic reproduction number (r0) of 3.0 and a herd immunity threshold (HIT) of 0.667 with and without vaccination. The infectious disease was expected to be controlled or eradicated when the total immunized population either through infection or vaccination reached the level predicted by the HIT. Outcomes of simulations were assessed at the time when the total immunized population reached the level predicted by the HIT, and at the end of simulations. Findings All three models performed likewise at the beginning of the transmission when sizes of infectious and recovered were relatively small as compared with the population size. The infectious disease modelled using the conventional SIR model appeared completely out of control even when the HIT was achieved in all scenarios with and without vaccination. The infectious disease modelled using Model A appeared to be controlled at the level predicted by the HIT in all scenarios with and without vaccination. Model B projected the infectious disease to be controlled at the level predicted by the HIT only at high vaccination rates. At lower vaccination rates or without vaccination, the level at which the infectious disease was controlled cannot be accurately predicted by the HIT. Conclusion Transmission dynamics of infectious diseases with herd immunity can accurately be modelled by allowing the transmission rate of infectious disease to decline along with the combined risk of contact infection. Model B provides a more credible framework for modelling infectious diseases with herd immunity in a randomly mixed population.

scholarly journals A preceding low-virulence strain pandemic inducing immunity against COVID-19

2020 ◽  
Author(s):  
Hagai Perets ◽  
Ruth Perets
Keyword(s):  
Population Size ◽  
Herd Immunity ◽  
Onset Time ◽  
Infection Model ◽  
P Value ◽  
Exposure Level ◽  
Strong Positive Correlation ◽  
Infection Level ◽  
Initial Onset ◽  
Containment Measures

Abstract The COVID-19 pandemic is thought to began in Wuhan, China in December 2019. Mobility analysis identified East-Asia and Oceania countries to be highly-exposed to COVID-19 spread, consistent with the earliest spread occurring in these regions. However, here we show that while a strong positive correlation between case-numbers and exposure level could be seen early-on as expected, at later times the infection-level is found to be negatively correlated with exposure-level. Moreover, the infection level is positively correlated with the population size, which is puzzling since it has not reached the level necessary for population-size to affect infection-level through herd immunity. These issues are resolved if a low-virulence Corona-strain (LVS) began spreading earlier in China outside of Wuhan, and later globally, providing immunity from the later appearing high-virulence strain (HVS). Following its spread into Wuhan, cumulative mutations gave rise to the emergence of an HVS, known as SARS-CoV-2, starting the COVID-19 pandemic. We model the co-infection by an LVS and an HVS and show that it can explain the evolution of the COVID-19 pandemic and the non-trivial dependence on the exposure level to China and the population-size in each country. We find that the LVS began its spread a few months before the onset of the HVS and that its spread doubling-time is \sim1.59\pm0.17 times slower than the HVS. Although more slowly spreading, its earlier onset allowed the LVS to spread globally before the emergence of the HVS. In particular, in countries exposed earlier to the LVS and/or having smaller population-size, the LVS could achieve herd-immunity earlier, and quench the later-spread HVS at earlier stages. We find our two-parameter (the spread-rate and the initial onset time of the LVS) can accurately explain the current infection levels (R^2=0.74); p-value (p) of 5.2x10^-13). Furthermore, countries exposed early should have already achieved herd-immunity. We predict that in those countries cumulative infection levels could rise by no more than 2-3 times the current level through local-outbreaks, even in the absence of any containment measures. We suggest several tests and predictions to further verify the double-strain co-infection model and discuss the implications of identifying the LVS.

Neurovestibular symptoms following space flight

2003 ◽  
Vol 13 (2-3) ◽  
pp. 93-102
Author(s):  
Kira Bacal ◽  
Roger Billica ◽  
Sheryl Bishop
Keyword(s):  
Space Flight ◽  
Flight Period ◽  
Blurred Vision ◽  
Space Motion Sickness ◽  
Mission Duration ◽  
Straight Line ◽  
Space Motion ◽  
Health Database ◽  
Using Data ◽  
Difficulty Walking

Neurovestibular symptoms experienced by astronauts in the post-flight period were examined using data from medical debriefs contained in the NASA Longitudinal Study of Astronaut Health database. Ten symptoms were identified (clumsiness, difficulty concentrating, persisting sensation aftereffects, nausea, vomiting, vertigo while walking, vertigo while standing, difficulty walking a straight line, blurred vision, and dry heaves), of which eight were crossed with twelve demographic parameters (mission duration, astronaut gender, age, one-g piloting experience, previous space flight experience, g-suit inflation, g-suit deflation, in-flight space motion sickness, in-flight exercise, post-flight exercise, mission role, fluid loading). Three symptoms were experienced by a majority of subjects, and another two by more than a quarter of the subjects. Intensity of the symptoms was mild, suggesting that they are unlikely to pose a risk to the crew during landing and the post-flight period. Seven of the symptoms and eight of the parameters under study were found to be significantly associated with each other.

Social Norms and Household Savings Rates in China

2018 ◽  
Vol 23 (5) ◽  
pp. 961-991
Author(s):  
Yvonne Jie Chen ◽  
Zhiwu Chen ◽  
Shijun He
Keyword(s):  
Social Norms ◽  
Filial Piety ◽  
Household Finance ◽  
Household Savings ◽  
Savings Rate ◽  
Model Predictions ◽  
Enforcement Mechanisms ◽  
Using Data ◽  
Period Model ◽  
Savings Rates

Abstract We study the effects of Confucian social norms on savings rates in China. In our simple two-period model, parents have the option to invest in either a risk-free asset or their children’s human capital. We assume that the filial piety norms and thus the enforcement mechanisms for supporting old-age parents differ across regions. Consequently, the probability of children’s non-performance of their repayment obligations to parents and the returns parents can expect from investing in their children vary. We test the model predictions using data from the China Household Finance Survey. We find that stronger Confucian social norms reduce the gap in the savings rate between families with sons and with daughters. Modeling default by children as a function of the prevailing social norms gives us the flexibility to study the impacts of declining Confucian influence on consumption–savings trends in China.

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