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Emerging and re-emerging infections such as SARS (2003) and pandemic H1N1 (2009) have caused concern for public health researchers and policy makers due to the increased burden of these diseases on health care systems. This concern has prompted the use of mathematical models to evaluate strategies to control disease spread, making these models inva...
Similar publications
Background:
To date, research on the indirect impact of the COVID-19 pandemic on the health of the population and the health-care system is scarce. We aimed to investigate the indirect effect of the COVID-19 pandemic on general practice health-care usage, and the subsequent diagnoses of common physical and mental health conditions in a deprived UK...
Citations
... By observing the real networks, differences among communities are found, when the individuals are divided into communities according to a certain property. For example, individuals communicate more frequently in communities belonging to young students than in communities belonging to the old in social networks, which indicates that heterogeneity does exist among communities [43]. Therefore, considering the heterogeneity among communities in reality, we construct a community structure model with adjustable heterogeneity among communities. ...
Real networks usually exhibit community structure characteristics, and communities have heterogeneity. In this paper, we construct a community network model with different average degree by considering the heterogeneity among communities. The community heterogeneity coefficient is proposed to measure the heterogeneity among communities. Based on homogeneous, heterogeneous, and mixed community networks, we study the influence of community heterogeneity on traffic dynamics. It is found that the more significant the community heterogeneity is, the smaller the traffic capacity is. We also studied the relationship between community heterogeneity coefficient and modularity, modularity and traffic capacity. It is found that the change of community heterogeneity coefficient has little effect on modularity, that is, with the increase in community heterogeneity coefficient, modularity increases slowly, and traffic capacity decreases.
... In addition to COVID-19, a variety of emerging infectious diseases in recent years, such as severe acute respiratory syndrome (SARS), influenza A virus subtype H1N1 (A/H1N1), Ebola virus disease (EVD), Middle East respiratory syndrome, and avian influenza, also pose serious threats to human health and life safety (4)(5)(6). Since the SARS outbreak in 2003, the government, research institutions, and public health departments have fully realized the importance of rapid identification and early intervention in infectious diseases (7,8). In the following years, China's public health system has undergone a series of adjustments. ...
Introduction
Modeling on infectious diseases is significant to facilitate public health policymaking. There are two main mathematical methods that can be used for the simulation of the epidemic and prediction of optimal early warning timing: the logistic differential equation (LDE) model and the more complex generalized logistic differential equation (GLDE) model. This study aimed to compare and analyze these two models.
Methods
We collected data on (coronavirus disease 2019) COVID-19 and four other infectious diseases and classified the data into four categories: different transmission routes, different epidemic intensities, different time scales, and different regions, using R² to compare and analyze the goodness-of-fit of LDE and GLDE models.
Results
Both models fitted the epidemic curves well, and all results were statistically significant. The R² test value of COVID-19 was 0.924 (p < 0.001) fitted by the GLDE model and 0.916 (p < 0.001) fitted by the LDE model. The R² test value varied between 0.793 and 0.966 fitted by the GLDE model and varied between 0.594 and 0.922 fitted by the LDE model for diseases with different transmission routes. The R² test values varied between 0.853 and 0.939 fitted by the GLDE model and varied from 0.687 to 0.769 fitted by the LDE model for diseases with different prevalence intensities. The R² test value varied between 0.706 and 0.917 fitted by the GLDE model and varied between 0.410 and 0.898 fitted by the LDE model for diseases with different time scales. The GLDE model also performed better with nation-level data with the R² test values between 0.897 and 0.970 vs. 0.731 and 0.953 that fitted by the LDE model. Both models could characterize the patterns of the epidemics well and calculate the acceleration weeks.
Conclusion
The GLDE model provides more accurate goodness-of-fit to the data than the LDE model. The GLDE model is able to handle asymmetric data by introducing shape parameters that allow it to fit data with various distributions. The LDE model provides an earlier epidemic acceleration week than the GLDE model. We conclude that the GLDE model is more advantageous in asymmetric infectious disease data simulation.
... for influenza, 3.0 for RSV, and 3.7-57.0 for measles) [27,31,[72][73][74][75][76]. Little is known about R0 for P. jirovecii. ...
Consolidated infection control measures imposed by the government and hospitals during COVID-19 pandemic resulted in a sharp decline of respiratory viruses. Based on the issue of whether Pneumocystis jirovecii could be transmitted by airborne and acquired from the environment, we assessed changes in P. jirovecii pneumonia (PCP) cases in a hospital setting before and after COVID-19. We retrospectively collected data of PCP-confirmed inpatients aged ≥18 years (N = 2922) in four university-affiliated hospitals between January 2015 and June 2021. The index and intervention dates were defined as the first time of P. jirovecii diagnosis and January 2020, respectively. We predicted PCP cases for post-COVID-19 and obtained the difference (residuals) between forecasted and observed cases using the autoregressive integrated moving average (ARIMA) and the Bayesian structural time-series (BSTS) models. Overall, the average of observed PCP cases per month in each year were 36.1 and 47.3 for pre- and post-COVID-19, respectively. The estimate for residuals in the ARIMA model was not significantly different in the total PCP-confirmed inpatients (7.4%, p = 0.765). The forecasted PCP cases by the BSTS model were not significantly different from the observed cases in the post-COVID-19 (−0.6%, 95% credible interval; −9.6~9.1%, p = 0.450). The unprecedented strict non-pharmacological interventions did not affect PCP cases.
... Our study's first limitation is that the survey may have been subject to recall and social conformity biases, but its longitudinal design enabled us to track the same respondents over time, reducing self-control bias. Second, caution should be exercised when generalizing our findings to other regions because Hong Kong was exposed to other disease outbreaks recently, such as 1997 avian influenza (40), 2003 SARS (41), and 2009 pandemic influenza (42). Nevertheless, our COVID-19 experience after those past outbreaks may be precedent to other countries, after the current CO-VID-19 pandemic. ...
To access temporal changes in psychobehavioral responses to the coronavirus disease (COVID-19) pandemic, we conducted a 5-round (R1-R5) longitudinal population-based online survey in Hong Kong during January-September 2020. Most respondents reported wearing masks (R1 99.0% to R5 99.8%) and performing hand hygiene (R1 95.8% to R5 97.7%). Perceived COVID-19 severity decreased significantly, from 97.4% (R1) to 77.2% (R5), but perceived self-susceptibility remained high (87.2%-92.8%). Female sex and anxiety were associated with greater adoption of social distancing. Intention to receive COVID-19 vaccines decreased significantly (R4 48.7% to R5 37.6%). Greater anxiety, confidence in vaccine, and collective responsibility and weaker complacency were associated with higher tendency to receive COVID-19 vaccines. Although its generalizability should be assumed with caution, this study helps to formulate health communication strategies and foretells the initial low uptake rate of COVID-19 vaccines, suggesting that social distancing should be maintained in the medium term.
... For instance, age population structure has been found to alter R 0 estimates for rubella, influenza and other diseases [15,16]. Broadly, host age effects are due to age-specific differences in contact patterns, host immune competency, susceptibility and transmission capability, and fitness costs [12,[17][18][19][20][21][22][23][24]. Overall, significantly less work has centered on the consequences of age structure in intermediate host populations. ...
Background
Schistosomiasis is a neglected tropical disease endemic in 54 countries. A major Schistosoma species, Schistosoma mansoni , is sustained via a life cycle that includes both human and snail hosts. Mathematical models of S. mansoni transmission, used to elucidate the complexities of the transmission cycle and estimate the impact of intervention efforts, often focus primarily on the human host. However, S. mansoni incurs physiological costs in snails that vary with the age of the snail when first infected. Snail demography and the age of snail infection could thus affect the force of infection experienced by humans, which is frequently used to predict the impact of various control strategies.
Methods
To address how these snail host and parasite interactions influence model predictions, we developed deterministic models of schistosomiasis transmission that include varying complexity in the snail population age structure. Specifically, we examined how model outputs, such as schistosome prevalence in human and snail populations, respond to the inclusion of snail age structure.
Results
Our models suggest that snail population age structure modifies the force of infection experienced by humans and the relationship between snail infection prevalence and corresponding human infection prevalence. There are significant differences in estimated snail infection, cercarial density and mean worm burden between models without snail population dynamics and those with snail populations, and between models with a homogeneous snail population and those with age stratification. The variation between finely age-stratified snail populations and those grouped into only juvenile and adult life stages is, however, minimal.
Conclusions
These results indicate that including snails and snail age structure in a schistosomiasis transmission model alters the relationship between snail and human infection prevalence. This highlights the importance of accounting for a heterogeneous intermediate host population in models of schistosomiasis transmission where the impact of proposed control measures is being considered.
... We note that practical use of the methods presented here at the start of an emerging outbreak to assess the outbreak risk might require the parameters governing pathogen transmission to be estimated directly from case notification data. A range of methods exist for estimating reproduction numbers in real-time during outbreaks [82,83,[102][103][104], including those designed for estimation in the early stochastic phase [105,106]. Practical use of the approaches that we have developed might also require the wide range of interventions that are introduced in outbreak responses to be integrated into the models explicitly. One way in which control can be included is to consider the effective reproduction number when the pathogen arrives in the system instead of the basic reproduction number, since the effective reproduction number accounts for interventions [26,[81][82][83][107][108][109]. ...
Forecasting whether or not initial reports of disease will be followed by a severe epidemic is an important component of disease management. Standard epidemic risk estimates involve assuming that infections occur according to a branching process and correspond to the probability that the outbreak persists beyond the initial stochastic phase. However, an alternative assessment is to predict whether or not initial cases will lead to a severe epidemic in which available control resources are exceeded. We show how this risk can be estimated by considering three practically relevant potential definitions of a severe epidemic; namely, an outbreak in which: (i) a large number of hosts are infected simultaneously; (ii) a large total number of infections occur; and (iii) the pathogen remains in the population for a long period. We show that the probability of a severe epidemic under these definitions often coincides with the standard branching process estimate for the major epidemic probability. However, these practically relevant risk assessments can also be different from the major epidemic probability, as well as from each other. This holds in different epidemiological systems, highlighting that careful consideration of how to classify a severe epidemic is vital for accurate epidemic risk quantification.
... For example, in real social networks, young students community interact with each other more frequently than elderly people community. Furthermore, studies have suggested that heterogeneity in contact patterns among individuals in heterogeneity communities has an important effect on the epidemic spreading [10]. In addition, many scholars are concerned about finding ways to suppress the spread of the virus, such as looking for more effective immune strategies and raising awareness of the individuals. ...
... Most of the models ignore the heterogeneity among communities, and how it affects the spread of epidemic is a practical and meaningful problem. In [10], by observing the real populations in the populated city Hong Kong, the researchers find out that the heterogeneity of contact patterns of individuals within and between different age groups is an important impacting factor in the transmission of infectious diseases, so they take into account the age structure of a population and the different contact patterns among individuals in different age groups and propose an age-structured model. When the groups of different ages mentioned in Ref. [10] are regarded as different communities, the whole population in the city can be viewed as a community structure network with heterogeneity. ...
... In [10], by observing the real populations in the populated city Hong Kong, the researchers find out that the heterogeneity of contact patterns of individuals within and between different age groups is an important impacting factor in the transmission of infectious diseases, so they take into account the age structure of a population and the different contact patterns among individuals in different age groups and propose an age-structured model. When the groups of different ages mentioned in Ref. [10] are regarded as different communities, the whole population in the city can be viewed as a community structure network with heterogeneity. In this section, we construct a new community structure network model with heterogeneity among communities based on random network, in which the average degree of each community may be different. ...
A large number of real world networks exhibit community structure, and different communities may often possess heterogeneity. In this paper, considering the heterogeneity among communities, we construct a new community network model in which the communities show significant differences in average degree. Based on this heterogeneous community network, we propose a novel mathematical epidemic model for each community and study the epidemic dynamics in this network model. We find that the location of the initial infection node only affects the spreading velocity and barely influences the epidemic prevalence. And the epidemic threshold of entire network decreases with the increase of heterogeneity among communities. Moreover, the epidemic prevalence increases with the increase of heterogeneity around the epidemic threshold, while the converse situation holds when the infection rate is much greater than the epidemic threshold.
The objective of this Personal View is to compare transmissibility, hospitalisation, and mortality rates for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with those of other epidemic coronaviruses, such as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), and pandemic influenza viruses. The basic reproductive rate (R0) for SARS-CoV-2 is estimated to be 2·5 (range 1·8–3·6) compared with 2·0–3·0 for SARS-CoV and the 1918 influenza pandemic, 0·9 for MERS-CoV, and 1·5 for the 2009 influenza pandemic. SARS-CoV-2 causes mild or asymptomatic disease in most cases; however, severe to critical illness occurs in a small proportion of infected individuals, with the highest rate seen in people older than 70 years. The measured case fatality rate varies between countries, probably because of differences in testing strategies. Population-based mortality estimates vary widely across Europe, ranging from zero to high. Numbers from the first affected region in Italy, Lombardy, show an all age mortality rate of 154 per 100 000 population. Differences are most likely due to varying demographic structures, among other factors. However, this new virus has a focal dissemination; therefore, some areas have a higher disease burden and are affected more than others for reasons that are still not understood. Nevertheless, early introduction of strict physical distancing and hygiene measures have proven effective in sharply reducing R0 and associated mortality and could in part explain the geographical differences.
Background
The novel influenza A (H1N1) pdm09 (A/H1N1pdm) pandemic of 2009-2010 had a great impact on society.
Objective
We analyzed data from the absentee survey, conducted in elementary schools of Oita City, to evaluate the A/H1N1pdm pandemic and to estimate the basic reproductive number (R0 ) of this novel strain.
Method
We summarized the overall absentee data and calculated the cumulative infection rate. Then, we classified the data into 3 groups according to school size: small (<300 students), medium (300–600 students), and large (>600 students). Last, we estimated the R0 value by using the Susceptible-Infected-Recovered (SIR) mathematical model.
Results
Data from 60 schools and 27,403 students were analyzed. The overall cumulative infection rate was 44.4%. There were no significant differences among the grades, but the cumulative infection rate increased as the school size increased, being 37.7%, 44.4%, and 46.6% in the small, medium, and large school groups, respectively. The optimal R0 value was 1.33, comparable with that previously reported. The data from the absentee survey were reliable, with no missing values. Hence, the R0 derived from the SIR model closely reflected the observed R0 . The findings support previous reports that school children are most susceptible to A/H1N1pdm virus infection and suggest that the scale of an outbreak is associated with the size of the school.
Conclusion
Our results provide further information about the A/H1N1pdm pandemic. We propose that an absentee survey should be implemented in the early stages of an epidemic, to prevent a pandemic.
Purpose of review:
Mathematical modeling approaches have brought important contributions to the study of pathogen spread in healthcare settings over the last 20 years. Here, we conduct a comprehensive systematic review of mathematical models of disease transmission in healthcare settings and assess the application of contact and patient transfer network data over time and their impact on our understanding of transmission dynamics of infections.
Recent findings:
Recently, with the increasing availability of data on the structure of interindividual and interinstitution networks, models incorporating this type of information have been proposed, with the aim of providing more realistic predictions of disease transmission in healthcare settings. Models incorporating realistic data on individual or facility networks often remain limited to a few settings and a few pathogens (mostly methicillin-resistant Staphylococcus aureus).
Summary:
To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.
