force of infection
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2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Julia Ledien ◽  
Zulma M. Cucunubá ◽  
Gabriel Parra-Henao ◽  
Eliana Rodríguez-Monguí ◽  
Andrew P. Dobson ◽  
...  

AbstractAge-stratified serosurvey data are often used to understand spatiotemporal trends in disease incidence and exposure through estimating the Force-of-Infection (FoI). Typically, median or mean FoI estimates are used as the response variable in predictive models, often overlooking the uncertainty in estimated FoI values when fitting models and evaluating their predictive ability. To assess how this uncertainty impact predictions, we compared three approaches with three levels of uncertainty integration. We propose a performance indicator to assess how predictions reflect initial uncertainty.In Colombia, 76 serosurveys (1980–2014) conducted at municipality level provided age-stratified Chagas disease prevalence data. The yearly FoI was estimated at the serosurvey level using a time-varying catalytic model. Environmental, demographic and entomological predictors were used to fit and predict the FoI at municipality level from 1980 to 2010 across Colombia.A stratified bootstrap method was used to fit the models without temporal autocorrelation at the serosurvey level. The predictive ability of each model was evaluated to select the best-fit models within urban, rural and (Amerindian) indigenous settings. Model averaging, with the 10 best-fit models identified, was used to generate predictions.Our analysis shows a risk of overconfidence in model predictions when median estimates of FoI alone are used to fit and evaluate models, failing to account for uncertainty in FoI estimates. Our proposed methodology fully propagates uncertainty in the estimated FoI onto the generated predictions, providing realistic assessments of both central tendency and current uncertainty surrounding exposure to Chagas disease.


2022 ◽  
Author(s):  
Nandadulal Bairagi ◽  
Abhijiit Majumder

Rate parameters are critical in estimating the covid burden using mathematical models. In the Covid-19 mathematical models, these parameters are assumed to be constant. However, uncertainties in these rate parameters are almost inevitable. In this paper, we study a stochastic epidemic model of the SARS-CoV-2 virus infection in the presence of vaccination in which some parameters fluctuate around its average value. Our analysis shows that if the stochastic basic reproduction number (SBRN) of the system is greater than unity, then there is a stationary distribution, implying the long-time disease persistence. A sufficient condition for disease eradication is also prescribed for which the exposed class goes extinct, followed by the infected class. The disease eradication criterion may not hold if the rate of vaccine-induced immunity loss increases or/and the force of infection increases. Using the Indian Covid-19 data, we estimated the model parameters and showed the future disease progression in the presence of vaccination. The disease extinction time is estimated under various conditions. It is revealed that the mean extinction time is an increasing function of both the force of infection and immunity loss rate and shows the lognormal distribution. We point out that disease eradication might not be possible even at a higher vaccination rate if the vaccine-induced immunity loss rate is high. Our observation thus indicates the endemicity of the disease for the existing vaccine efficacy. The disease eradication is possible only with a higher vaccine efficacy or a reduced infection rate.


2021 ◽  
Vol 6 ◽  
pp. 138
Author(s):  
Eleanor M. Rees ◽  
Naomi R. Waterlow ◽  
Rachel Lowe ◽  
Adam J. Kucharski ◽  

Background: The duration of immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still uncertain, but it is of key clinical and epidemiological importance. Seasonal human coronaviruses (HCoV) have been circulating for longer and, therefore, may offer insights into the long-term dynamics of reinfection for such viruses. Methods: Combining historical seroprevalence data from five studies covering the four circulating HCoVs with an age-structured reverse catalytic model, we estimated the likely duration of seropositivity following seroconversion. Results: We estimated that antibody persistence lasted between 0.9 (95% Credible interval: 0.6 - 1.6) and 3.8 (95% CrI: 2.0 - 7.4) years. Furthermore, we found the force of infection in older children and adults (those over 8.5 [95% CrI: 7.5 - 9.9] years) to be higher compared with young children in the majority of studies. Conclusions: These estimates of endemic HCoV dynamics could provide an indication of the future long-term infection and reinfection patterns of SARS-CoV-2.


2021 ◽  
Vol 17 (12) ◽  
pp. e1009697
Author(s):  
Fuminari Miura ◽  
Ka Yin Leung ◽  
Don Klinkenberg ◽  
Kylie E. C. Ainslie ◽  
Jacco Wallinga

For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible allocation schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest. We use an approximation method to reconstruct the age-specific transmission intensity (the next generation matrix), and express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. This approach to allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies.


2021 ◽  
Vol 6 ◽  
pp. 138
Author(s):  
Eleanor M. Rees ◽  
Naomi R. Waterlow ◽  
Rachel Lowe ◽  
Adam J. Kucharski ◽  

Background: The duration of immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still uncertain, but it is of key clinical and epidemiological importance. Seasonal human coronaviruses (HCoV) have been circulating for longer and, therefore, may offer insights into the long-term dynamics of reinfection for such viruses. Methods: Combining historical seroprevalence data from five studies covering the four circulating HCoVs with an age-structured reverse catalytic model, we estimated the likely duration of seropositivity following seroconversion. Results: We estimated that antibody persistence lasted between 0.9 (95% Credible interval: 0.6 - 1.6) and 3.8 (95% CrI: 2.0 - 7.4) years. Furthermore, we found the force of infection in older children and adults (those over 8.5 [95% CrI: 7.5 - 9.9] years) to be higher compared with young children in the majority of studies. Conclusions: These estimates of endemic HCoV dynamics could provide an indication of the future long-term infection and reinfection patterns of SARS-CoV-2.


2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S597-S598
Author(s):  
Nele Plock ◽  
Jos Lommerse ◽  
Brian M Maas ◽  
Jingxian Chen ◽  
Francesco Bellanti ◽  
...  

Abstract Background MK-1654 is a respiratory syncytial virus (RSV) F glycoprotein neutralizing monoclonal antibody under development to prevent RSV infection in infants. A model-based meta-analysis (MBMA) describing the relationship between RSV serum neutralizing activity (SNA) and clinically relevant endpoints (e.g. incidence rates) in humans, including lower respiratory tract infection (LRI) in infants, was presented previously. This model accounted for variable exposure to RSV over the course of the season through a force-of-infection (FOI) function modulating the overall risk of RSV infection over time. The objective of the current work was to determine whether variations in regional seasonality would impact the efficacy of a clinical trial evaluating MK-1654. Methods A FOI function to describe the degree of RSV exposure as a function of time was created by fitting epidemiological data to a Gaussian function added to a constant baseline value. Clinical trial simulations were conducted using the MBMA to predict seasonal incidence rates (IR) of RSV medically attended lower-respiratory tract infection (MALRI) and efficacies for a range of MK-1654 doses in both temperate and tropical regions. Results Epidemiological data was well captured by the FOI function. Clinical trial simulations indicated that seasonal IRs of RSV were sensitive to differences in the FOI represented by temperate and tropical regions; however, there was no substantial impact on efficacies across MK-1654 dose levels. Consistent with predictions for a temperate climate, MK-1654, when administered at the start of the RSV season in a region with a tropical climate, was also predicted to maintain high efficacy ( > 75%) for the prevention of RSV MALRI for 150 days. Conclusion Simulations indicated that while FOI is a substantial driver of overall RSV incidence rates, MK-1654 efficacy in a late-stage clinical trial is likely to be high, regardless of regional variations in RSV. Disclosures Nele Plock, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Jos Lommerse, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Brian M. Maas, PharmD, Merck & Co., Inc. (Employee, Shareholder) Jingxian Chen, PhD, Merck & Co., Inc. (Employee, Shareholder) Francesco Bellanti, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Li Qin, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Han Witjes, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Philippe Pierrillas, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Radha Railkar, PhD, Merck & Co., Inc. (Employee, Shareholder) Antonios O. Aliprantis, MD, PhD, Merck & Co., Inc. (Employee, Shareholder) Kalpit A. Vora, PhD, Merck & Co., Inc. (Employee, Shareholder) Wei Gao, PhD, Merck & Co., Inc. (Employee, Shareholder) Luzelena Caro, PhD, Merck & Co., Inc. (Employee, Shareholder) S. Y. Amy Cheung, PhD, Certara (Employee, Shareholder) Jeffrey R. Sachs, PhD, Merck & Co., Inc. (Employee, Shareholder)


2021 ◽  
Vol 15 (10) ◽  
pp. e0009385
Author(s):  
Sean M. Moore

Japanese encephalitis virus (JEV) is a major cause of neurological disability in Asia and causes thousands of severe encephalitis cases and deaths each year. Although Japanese encephalitis (JE) is a WHO reportable disease, cases and deaths are significantly underreported and the true burden of the disease is not well understood in most endemic countries. Here, we first conducted a spatial analysis of the risk factors associated with JE to identify the areas suitable for sustained JEV transmission and the size of the population living in at-risk areas. We then estimated the force of infection (FOI) for JE-endemic countries from age-specific incidence data. Estimates of the susceptible population size and the current FOI were then used to estimate the JE burden from 2010 to 2019, as well as the impact of vaccination. Overall, 1,543.1 million (range: 1,292.6-2,019.9 million) people were estimated to live in areas suitable for endemic JEV transmission, which represents only 37.7% (range: 31.6-53.5%) of the over four billion people living in countries with endemic JEV transmission. Based on the baseline number of people at risk of infection, there were an estimated 56,847 (95% CI: 18,003-184,525) JE cases and 20,642 (95% CI: 2,252-77,204) deaths in 2019. Estimated incidence declined from 81,258 (95% CI: 25,437-273,640) cases and 29,520 (95% CI: 3,334-112,498) deaths in 2010, largely due to increases in vaccination coverage which have prevented an estimated 314,793 (95% CI: 94,566-1,049,645) cases and 114,946 (95% CI: 11,421-431,224) deaths over the past decade. India had the largest estimated JE burden in 2019, followed by Bangladesh and China. From 2010-2019, we estimate that vaccination had the largest absolute impact in China, with 204,734 (95% CI: 74,419-664,871) cases and 74,893 (95% CI: 8,989-286,239) deaths prevented, while Taiwan (91.2%) and Malaysia (80.1%) had the largest percent reductions in JE burden due to vaccination. Our estimates of the size of at-risk populations and current JE incidence highlight countries where increasing vaccination coverage could have the largest impact on reducing their JE burden.


BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Joseph R. Biggs ◽  
Ava Kristy Sy ◽  
Katharine Sherratt ◽  
Oliver J. Brady ◽  
Adam J. Kucharski ◽  
...  

Abstract Background Stratifying dengue risk within endemic countries is crucial for allocating limited control interventions. Current methods of monitoring dengue transmission intensity rely on potentially inaccurate incidence estimates. We investigated whether incidence or alternate metrics obtained from standard, or laboratory, surveillance operations represent accurate surrogate indicators of the burden of dengue and can be used to monitor the force of infection (FOI) across urban centres. Methods Among those who reported and resided in 13 cities across the Philippines, we collected epidemiological data from all dengue case reports between 2014 and 2017 (N 80,043) and additional laboratory data from a cross-section of sampled case reports (N 11,906) between 2014 and 2018. At the city level, we estimated the aggregated annual FOI from age-accumulated IgG among the non-dengue reporting population using catalytic modelling. We compared city-aggregated FOI estimates to aggregated incidence and the mean age of clinically and laboratory diagnosed dengue cases using Pearson’s Correlation coefficient and generated predicted FOI estimates using regression modelling. Results We observed spatial heterogeneity in the dengue average annual FOI across sampled cities, ranging from 0.054 [0.036–0.081] to 0.249 [0.223–0.279]. Compared to FOI estimates, the mean age of primary dengue infections had the strongest association (ρ −0.848, p value<0.001) followed by the mean age of those reporting with warning signs (ρ −0.642, p value 0.018). Using regression modelling, we estimated the predicted annual dengue FOI across urban centres from the age of those reporting with primary infections and revealed prominent spatio-temporal heterogeneity in transmission intensity. Conclusions We show the mean age of those reporting with their first dengue infection or those reporting with warning signs of dengue represent superior indicators of the dengue FOI compared to crude incidence across urban centres. Our work provides a framework for national dengue surveillance to routinely monitor transmission and target control interventions to populations most in need.


2021 ◽  
Vol 5 ◽  
pp. 116
Author(s):  
Simon E.F. Spencer ◽  
Oliver Laeyendecker ◽  
Louise Dyson ◽  
Yu-Hsiang Hsieh ◽  
Eshan U. Patel ◽  
...  

Background: Our understanding of pathogens and disease transmission has improved dramatically over the past 100 years, but coinfection, how different pathogens interact with each other, remains a challenge. Cross-sectional serological studies including multiple pathogens offer a crucial insight into this problem.  Methods: We use data from three cross-sectional serological surveys (in 2003, 2007 and 2013) in a Baltimore emergency department to predict the prevalence for HIV, hepatitis C virus (HCV) and herpes simplex virus, type 2 (HSV2), in a fourth survey (in 2016). We develop a mathematical model to make this prediction and to estimate the incidence of infection and coinfection in each age and ethnic group in each year. Results: Overall we find a much stronger age cohort effect than a time effect, so that, while incidence at a given age may decrease over time, individuals born at similar times experience a more constant force of infection over time. Conclusions: These results emphasise the importance of age-cohort counselling and early intervention while people are young. Our approach adds value to data such as these by providing age- and time-specific incidence estimates which could not be obtained any other way, and allows forecasting to enable future public health planning.


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