scholarly journals Reconstructing the COVID-19 epidemic in Delhi, India: infection attack rate and reporting of deaths

Author(s):  
Margarita Pons-Salort ◽  
Jacob John ◽  
Oliver J Watson ◽  
Nicholas F Brazeau ◽  
Robert Verity ◽  
...  

India reported over 10 million COVID-19 cases and 149,000 deaths in 2020. To estimate exposure and the potential for further spread, we used a SARS-CoV-2 transmission model fit to seroprevalence data from three serosurveys in Delhi and the time-series of reported deaths to reconstruct the epidemic. The cumulative proportion of the population estimated infected was 48.7% (95% CrI 22.1% - 76.8%) by end-September 2020. Using an age-adjusted overall infection fatality ratio (IFR) based on age-specific estimates from mostly high-income countries (HICs), we estimate that 15.0% (95% CrI 9.3% - 34.0%) of COVID-19 deaths were reported. This indicates either under-reporting of COVID-19 deaths and/or a lower age-specific IFR in India compared with HICs. Despite the high attack rate of SARS-CoV-2, a third wave occurred in late 2020, suggesting that herd immunity was not yet reached. Future dynamics will strongly depend on the duration of immunity and protection against new variants.

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.


Author(s):  
Sudarshan Ramaswamy ◽  
Meera Dhuria ◽  
Sumedha M. Joshi ◽  
Deepa H Velankar

Introduction: Epidemiological comprehension of the COVID-19 situation in India can be of great help in early prediction of any such indications in other countries and possibilities of the third wave in India as well. It is essential to understand the impact of variant strains in the perspective of the rise in daily cases during the second wave – Whether the rise in cases witnessed is due to the reinfections or the surge is dominated by emergence of mutants/variants and reasons for the same. Overall objective of this study is to predict early epidemiological indicators which can potentially lead to COVID-19 third wave in India. Methodology: We analyzed both the first and second waves of COVID-19 in India and using the data of India’s SARS-CoV-2 genomic sequencing, we segregated the impact of the Older Variant (OV) and the other major variants (VOI / VOC).  Applying Kermack–McKendrick SIR model to the segregated data progression of the epidemic in India was plotted in the form of proportion of people infected. An equation to explain herd immunity thresholds was generated and further analyzed to predict the possibilities of the third wave. Results: Considerable difference in ate of progression of the first and second wave was seen. The study also ascertains that the rate of infection spread is higher in Delta variant and is expected to have a higher threshold (>2 times) for herd immunity as compared to the OV. Conclusion: Likelihood of the occurrence of the third wave seems unlikely based on the current analysis of the situation, however the possibilities cannot be ruled out. Understanding the epidemiological details of the first and second wave helped in understanding the focal points responsible for the surge in cases during the second wave and has given further insight into the future.


Author(s):  
Alexandra Teslya ◽  
Thi Mui Pham ◽  
Noortje G. Godijk ◽  
Mirjam E. Kretzschmar ◽  
Martin C.J. Bootsma ◽  
...  

AbstractBackgroundWith new cases of COVID-19 surging around the world, many countries have to prepare for moving beyond the containment phase. Prediction of the effectiveness of non-case-based interventions for mitigating, delaying or preventing the epidemic is urgent, especially for countries affected by the ongoing seasonal influenza activity.MethodsWe developed a transmission model to evaluate the impact of self-imposed prevention measures (handwashing, mask-wearing, and social distancing) due to the spread of COVID-19 awareness and of short-term government-imposed social distancing on the peak number of diagnoses, attack rate and time until the peak number of diagnoses.FindingsFor fast awareness spread in the population, self-imposed measures can significantly reduce the attack rate, diminish and postpone the peak number of diagnoses. A large epidemic can be prevented if the efficacy of these measures exceeds 50%. For slow awareness spread, self-imposed measures reduce the peak number of diagnoses and attack rate but do not affect the timing of the peak. Early implementation of short-term government interventions can only delay the peak (by at most 7 months for a 3-month intervention).InterpretationHandwashing, mask-wearing and social distancing as a reaction to information dissemination about COVID-19 can be effective strategies to mitigate and delay the epidemic. We stress the importance of rapidly spreading awareness on the use of these self-imposed prevention measures in the population. Early-initiated short-term government-imposed social distancing can buy time for healthcare systems to prepare for an increasing COVID-19 burden.FundingThis research was funded by ZonMw project 91216062, One Health EJP H2020 project 773830, Aidsfonds project P-29704.Research in contextEvidence before this studyEvidence to date suggests that containment of SARS-CoV-2 using quarantine, travel restrictions, isolation of symptomatic cases, and contact tracing may need to be supplemented by other interventions. Given its rapid spread across the world and immense implications for public health, it is urgent to understand whether non-case-based interventions can mitigate, delay or even prevent a COVID-19 epidemic. One such strategy is a broader-scale contact rate reduction enforced by governments which was used during previous outbreaks, e.g., the 1918 influenza pandemic and the 2009 influenza A/H1N1 pandemic in Mexico. Alternatively, governments and media may stimulate self-imposed prevention measures (handwashing, mask-wearing, and social distancing) by generating awareness about COVID-19, especially when economic and societal consequences are taken into account. Both of these strategies may have a significant impact on the outbreak dynamics. Currently, there are no comparative studies that investigate their viability for controlling a COVID-19 epidemic.Added value of this studyUsing a transmission model parameterized with current best estimates of epidemiological parameters, we evaluated the impact of handwashing, mask-wearing, and social distancing due to COVID-19 awareness and of government-imposed social distancing on the peak number of diagnoses, attack rate, and time until the peak number of diagnoses. We show that a short-term (1-3 months) government intervention initiated early into the outbreak can only delay the peak number of diagnoses but neither alters its magnitude nor the attack rate. Our analyses also highlight the importance of spreading awareness about COVID-19 in the population, as the impact of self-imposed measures is strongly dependent on it. When awareness spreads fast, simple self-imposed measures such as handwashing are more effective than short-term government intervention. Self-imposed measures do not only diminish and postpone the peak number of diagnoses, but they can prevent a large epidemic altogether when their efficacy is sufficiently high (above 50%). Qualitatively, these results will aid public health professionals to compare and select interventions for designing effective outbreak control policies.Implications of all available evidenceOur results highlight that dissemination of evidence-based information about effective prevention measures (hand-washing, mask-wearing, and self-imposed social distancing) can be a key strategy for mitigating and postponing a COVID-19 epidemic. Government interventions (e.g., closing schools and prohibiting mass gatherings) implemented early into the epidemic and lasting for a short-time can only buy time for healthcare systems to prepare for an increasing COVID-19 burden.


2019 ◽  
Author(s):  
Jonathan S. Nguyen-Van-Tam ◽  
Ben Killingley ◽  
Joanne Enstone ◽  
Michael Hewitt ◽  
Jovan Pantelic ◽  
...  

AbstractUncertainty about the importance of influenza transmission by airborne droplet nuclei generates controversy for infection control. Human challenge-transmission studies have been supported as the most promising approach to fill this knowledge gap. Healthy, seronegative volunteer ‘Donors’ (n=52) were randomly selected for intranasal challenge with influenza A/Wisconsin/67/2005 (H3N2). ‘Recipients’ randomized to Intervention (IR, n=40) or Control (CR, n=35) groups were exposed to Donors for four days. IRs wore face shields and hand sanitized frequently to limit large droplet and contact transmission. One transmitted infection was confirmed by serology in a CR, yielding a secondary attack rate of 2.9% among CR, 0% in IR (p=0.47 for group difference), and 1.3% overall, significantly less than 16% (p<0.001) expected based on a proof-of-concept study secondary attack rate and considering that there were twice as many Donors and days of exposure. The main difference between these studies was mechanical building ventilation in the follow-on study, suggesting a possible role for aerosols.Author summaryUnderstanding the relative importance of influenza modes of transmission informs strategic use of preventive measures to reduce influenza risk in high-risk settings such as hospitals and is important for pandemic preparedness. Given the increasing evidence from epidemiological modelling, exhaled viral aerosol, and aerobiological survival studies supporting a role for airborne transmission and the potential benefit of respirators (and other precautions designed to prevent inhalation of aerosols) versus surgical masks (mainly effective for reducing exposure to large droplets) to protect healthcare workers, more studies are needed to evaluate the extent of risk posed airborne versus contact and large droplet spray transmission modes. New human challenge-transmission studies should be carefully designed to overcome limitations encountered in the current study. The low secondary attack rate reported herein also suggests that the current challenge-transmission model may no longer be a more promising approach to resolving questions about transmission modes than community-based studies employing environmental monitoring and newer, state-of-the-art deep sequencing-based molecular epidemiological methods.


2021 ◽  
Author(s):  
Tarcisio Rocha Filho ◽  
José Mendes ◽  
Carson Chow ◽  
James Phillips ◽  
Antônio Cordeiro ◽  
...  

Abstract We introduce a compartmental model with age structure to study the dynamics of the SARS-COV−2 pandemic. The contagion matrix in the model is given by the product of a probability per contact with a contact matrix explicitly taking into account the contact structure among different age groups. The probability of contagion per contact is considered as time dependent to represent non-pharmaceutical interventions, and is fitted from the time series of deaths. The approach is used to study the evolution of the COVID−19 pandemic in the main Brazilian cities and compared to two good quality serological surveys. We also discuss with some detail the case of the city of Manaus which raised special attention due to a previous report of three-quarters attack rate by the end of 2020. We discuss estimates for Manaus and all Brazilian cities with a total population of more than one million. We also estimate the attack rate with respect to the total population, in each Brazilian state by January, 1 st 2021 and May, 23 2021.


2021 ◽  
Author(s):  
Kian Boon Law ◽  
Kalaiarasu M Peariasamy ◽  
Hishamshah Ibrahim ◽  
Noor Hisham Abdullah

Abstract The risk of contact infection among susceptible individuals in a randomly mixed population can be reduced by the presence of immune individuals and this principle forms the fundamental of herd immunity. The conventional susceptible-infectious-recovered (SIR) model features an infection-induced herd immunity model, but does not include the reducing risk of contact infection among susceptible individuals in the transmission model, therefore tends to overestimate the transmission dynamics of infectious diseases. Here we show that the reducing risk of contact infection among susceptible individuals can be achieved by incorporating the proportion of susceptible individuals (model A) or the inverse of proportion of recovered individuals (model B) in the force of infection of the SIR model. We numerically simulated the conventional SIR model and both new SIR models A and B under the exact condition with a basic reproduction number of 3·0. Prior to the numerical simulation, the threshold for the eradication of infectious disease through herd immunity was expected to be 0·667 (66·7%) for all three models. All three models performed likewise at the initial stage of disease transmission. In the conventional SIR model, the infectious disease subsided when 94·0 % of the population had been infected and recovered, way above the expected threshold for eradication and control of the infectious disease. Both models A and B simulated the infectious disease to diminish when 66·7% and 75·6% of the population had been infected, showing herd immunity might protect more susceptible individuals from the infectious disease as compared to the projection generated by the conventional SIR. Our study shows that model A provides a better framework for modelling herd immunity through vaccination, while model B provides a better framework for modelling herd immunity through infection. Both models overcome the insufficiency of the conventional SIR model in attaining the effect of herd immunity in modelling outputs, which is important and relevant for modelling infectious disease, such as the COVID-19 in a randomly mixed population.


2013 ◽  
Vol 141 (8) ◽  
pp. 1572-1584 ◽  
Author(s):  
M. O. MILBRATH ◽  
I. H. SPICKNALL ◽  
J. L. ZELNER ◽  
C. L. MOE ◽  
J. N. S. EISENBERG

SUMMARYNorovirus is a common cause of gastroenteritis in all ages. Typical infections cause viral shedding periods of days to weeks, but some individuals can shed for months or years. Most norovirus risk models do not include these long-shedding individuals, and may therefore underestimate risk. We reviewed the literature for norovirus-shedding duration data and stratified these data into two distributions: regular shedding (mean 14–16 days) and long shedding (mean 105–136 days). These distributions were used to inform a norovirus transmission model that predicts the impact of long shedders. Our transmission model predicts that this subpopulation increases the outbreak potential (measured by the reproductive number) by 50–80%, the probability of an outbreak by 33%, the severity of transmission (measured by the attack rate) by 20%, and transmission duration by 100%. Characterizing and understanding shedding duration heterogeneity can provide insights into community transmission that can be useful in mitigating norovirus risk.


2007 ◽  
Vol 9 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Nikhil S. Padhye ◽  
Sandra K. Hanneman

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.


2018 ◽  
Author(s):  
J. Daniel Kelly ◽  
Lee Worden ◽  
Rae Wannier ◽  
Nicole A. Hoff ◽  
Patrick Mukadi ◽  
...  

AbstractBackgroundAs of May 27, 2018, 54 cases of Ebola virus disease (EVD) were reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the current outbreak size and duration with and without vaccine use.MethodsWe modeled Ebola virus transmission using a stochastic branching process model with a negative binomial distribution, using both estimates of reproduction number R declining from supercritical to subcritical derived from past Ebola outbreaks, as well as a particle filtering method to generate a probabilistic projection of the future course of the outbreak conditioned on its reported trajectory to date; modeled using 0%, 44%, and 62% estimates of vaccination coverage. Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize a regression model predicting the outbreak size from the number of observed cases from April 4 to May 27.ResultsWith the stochastic transmission model, we projected a median outbreak size of 78 EVD cases (95% credible interval: 52, 125.4), 86 cases (95% credible interval: 53, 174.3), and 91 cases (95% credible interval: 52, 843.5), using 62%, 44%, and 0% estimates of vaccination coverage. With the regression model, we estimated a median size of 85.0 cases (95% prediction interval: 53.5, 216.6).ConclusionsThis outbreak has the potential to be the largest outbreak in DRC since 2007. Vaccines are projected to limit outbreak size and duration but are only part of prevention, control, and care strategies.


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