The time required to estimate the case fatality ratio of influenza using only the tip of an iceberg: joint estimation of the virulence and the transmission potential

COVID-19 and the flu: data simulations and computational modelling to guide public health strategies

BACKGROUND

Pandemics threaten lives and economies. This article addresses the global threat of the anticipated overlap of COVID-19 with seasonal-influenza.

OBJECTIVES

Scientific evidence based on simulation methodology is presented to reveal the impact of a dual outbreak, with scenarios intended for propagation analysis. This article aims at researchers, clinicians of family medicine, general practice and policy-makers worldwide. The implications for the clinical practice of primary health care are discussed. Current research is an effort to explore new directions in epidemiology and health services delivery.

METHODS

Projections consisted of machine learning, dynamic modelling algorithms and whole simulations. Input data consisted of global indicators of infectious diseases. Four simulations were run for '20% versus 60% flu-vaccinated populations' and '10 versus 20 personal contacts'. Outputs consisted of numerical values and mathematical graphs. Outputs consisted of numbers for 'never infected', 'vaccinated', 'infected/recovered', 'symptomatic/asymptomatic' and 'deceased' individuals. Peaks, percentages, R0, durations are reported.

RESULTS

The best-case scenario was one with a higher flu-vaccination rate and fewer contacts. The reverse generated the worst outcomes, likely to disrupt the provision of vital community services. Both measures were proven effective; however, results demonstrated that 'increasing flu-vaccination rates' is a more powerful strategy than 'limiting social contacts'.

CONCLUSIONS

Results support two affordable preventive measures: (i) to globally increase influenza-vaccination rates, (ii) to limit the number of personal contacts during outbreaks. The authors endorse changing practices and research incentives towards multidisciplinary collaborations. The urgency of the situation is a call for international health policy to promote interdisciplinary modern technologies in public health engineering.

Delay-Adjusted Age-Specific COVID-19 Case Fatality Rates in a High Testing Setting: South Korea, February 2020 to February 2021

In South Korea, a country with a high coronavirus disease 19 (COVID-19) testing rate, a total of 87,324 COVID-19 cases, including 1562 deaths, have been recorded as of 23 February 2021. This study assessed the delay-adjusted COVID-19 case fatality risk (CFR), including data from the second and third waves. A statistical method was applied to the data from 20 February 2021 through 23 February 2021 to minimize bias in the crude CFR, accounting for the survival interval as the lag time between disease onset and death. The resulting overall delay-adjusted CFR was 1.97% (95% credible interval: 1.94–2.00%). The delay-adjusted CFR was highest among adults aged ≥80 years and 70–79 years (22.88% and 7.09%, respectively). The cumulative incidence rate was highest among individuals aged ≥80 years and 60–69 years. The cumulative mortality rate was highest among individuals aged ≥80 years and 70–79 years (47 and 12 per million, respectively). In South Korea, older adults are being disproportionately affected by COVID-19 with a high death rate, although the incidence rate among younger individuals is relatively high. Interventions to prevent COVID-19 should target older adults to minimize the number of deaths.

Estimation of the case fatality rate based on stratification for the COVID-19 outbreak

This work is motivated by the recent worldwide pandemic of the novel coronavirus disease (COVID-19). When an epidemiological disease is prevalent, estimating the case fatality rate, the proportion of deaths out of the total cases, accurately and quickly is important as the case fatality rate is one of the crucial indicators of the risk of a disease. In this work, we propose an alternative estimator of the case fatality rate that provides more accurate estimate during an outbreak by reducing the downward bias (underestimation) of the naive CFR, the proportion of deaths out of confirmed cases at each time point, which is the most commonly used estimator due to the simplicity. The proposed estimator is designed to achieve the availability of real-time update by using the commonly reported quantities, the numbers of confirmed, cured, deceased cases, in the computation. To enhance the accuracy, the proposed estimator adapts a stratification, which allows the estimator to use information from heterogeneous strata separately. By the COVID-19 cases of China, South Korea and the United States, we numerically show the proposed stratification-based estimator plays a role of providing an early warning about the severity of a epidemiological disease that estimates the final case fatality rate accurately and shows faster convergence to the final case fatality rate.

Clinician-researcher's perspectives on clinical research during the COVID19 pandemic

Objectives

The outcome of well-performed clinical research is essential for evidence-based patient management during pandemics. However, conducting clinical research amidst a pandemic requires researchers to balance clinical and research demands. We seek to understand the values, experiences, and beliefs of physicians working at the onset of the COVID-19 pandemic in order to inform clinical research planning. We aim to understand whether pandemic settings affect physician comfort with research practices, and how physician experiences shape their understanding of research in a pandemic setting.

Methods

A survey tool was adapted to evaluate familiarity and comfort with research during a pandemic. A cross-sectional, online questionnaire was distributed across Canadian research networks early in the COVID-19 outbreak. The survey was administered between March 11^th and 17^th, 2020, during a time of local transmission but prior to the surge of cases. We aimed to recruit into the survey physicians in infectious disease and critical care research networks across Canada.

Results

Of the 133 physician respondents, 131 (98%) considered it important to conduct clinical research during the COVID-19 pandemic. Respondents were more accepting of adaptations to the research process in during a pandemic compared to in a non-pandemic setting, including conducting research with deferred consent (χ² = 8.941, 95% CI: -0.264, -0.085, p = 0.003), using non-identifiable observational data with a waiver of consent with a median score of 97 out of 100 (IQR: 79.25–100) vs median 87 out of 100 (IQR: 63–79) (95% CI: -12.43, 0.054, p = 0.052). The majority felt that research quality is not compromised during pandemics.

Conclusions

Physicians consider it important to conduct research during a pandemic, highlighting the need to expedite research activities in pandemic settings. Respondents were more accepting of adaptations to the research process for research conducted during a pandemic, compared to that conducted in its absence of a pandemic.

COVID-19: Living through Another Pandemic

Novel beta-coronavirus SARS-CoV-2 is the pathogenic agent responsible for coronavirus disease-2019 (COVID-19), a globally pandemic infectious disease. Due to its high virulence and the absence of immunity among the general population, SARS-CoV-2 has quickly spread to all countries. This pandemic highlights the urgent unmet need to expand and focus our research tools on what are considered "neglected infectious diseases" and to prepare for future inevitable pandemics. This global emergency has generated unprecedented momentum and scientific efforts around the globe unifying scientists from academia, government and the pharmaceutical industry to accelerate the discovery of vaccines and treatments. Herein, we shed light on the virus structure and life cycle and the potential therapeutic targets in SARS-CoV-2 and briefly refer to both active and passive immunization modalities, drug repurposing focused on speed to market, and novel agents against specific viral targets as therapeutic interventions for COVID-19.

Online estimation of the case fatality rate using a run-off triangle data approach: An application to the Korean MERS outbreak in 2015

This work is motivated by the recent Korean Middle East respiratory syndrome outbreak. We propose an easy online estimation procedure for the case fatality rate, ie, the proportion of deaths among the total cases during the course of an epidemic disease, which is an important indicator of the severity of a disease. The key step in our procedure is representing the data with the run-off triangle, which simultaneously takes into account two time axes, namely, the calendar and disease-duration times. We restructure the original data into run-off triangle data, where the cells contain the numbers of cured patients, deceased patients, and patients still having the disease at a given combination of calendar and disease-duration times. Based on the restructured run-off triangle data, we propose an online estimator of the case fatality rate. We numerically show the advantages of the proposed estimator compared to the existing estimators in the literature. Finally, we apply our procedure to the 2015 Korean Middle East respiratory syndrome outbreak data.

Real-time quantification of the next-generation matrix and age-dependent forecasting of pandemic influenza H1N1 2009 in Japan

PURPOSE

To quantify the age-dependent next-generation matrix (NGM) for the 2009 H1N1 influenza pandemic and forecast the age-stratified cumulative incidence in Japan.

METHODS

Using a renewal equation model that describes the time evolution of the 2009 H1N1 influenza pandemic, we derive the likelihood function to estimate parameters of the NGM and reporting coverage. Comparing the Akaike Information Criterion of models using empirically observed data from the 2009 pandemic in Gifu, Japan, we excluded redundant parameters and identified the three best models that were parameterized in different ways.

RESULTS

The initial proportions of susceptible populations were suggested as redundant information to be inferred. The three models selected successfully captured the order of the age-dependent cumulative incidence. We found that the time required for reliable estimation of age-dependent cumulative incidence was at least 180 days.

CONCLUSIONS

To forecast the age-dependent cumulative incidence reliably following the estimation of the NGM and reporting coverage, we need empirically observed data for more than 5 months from the start of the epidemic, which is likely to be after the peak. To increase the practical efficacy in forecasting the cumulative incidence, additional data and approaches are required.

JOINT ESTIMATION OF THE TRANSMISSIBILITY AND SEVERITY OF EBOLA VIRUS DISEASE IN REAL TIME

The effective reproduction number [Formula: see text], the average number of secondary cases that are generated by a single primary case at calendar time [Formula: see text], plays a critical role in interpreting the temporal transmission dynamics of an infectious disease epidemic, while the case fatality risk (CFR) is an indispensable measure of the severity of disease. In many instances, [Formula: see text] is estimated using the reported number of cases (i.e., the incidence data), but such report often does not arrive on time, and moreover, the rate of diagnosis could change as a function of time, especially if we handle diseases that involve substantial number of asymptomatic and mild infections and large outbreaks that go beyond the local capacity of reporting. In addition, CFR is well known to be prone to ascertainment bias, often erroneously overestimated. In this paper, we propose a joint estimation method of [Formula: see text] and CFR of Ebola virus disease (EVD), analyzing the early epidemic data of EVD from March to October 2014 and addressing the ascertainment bias in real time. To assess the reliability of the proposed method, coverage probabilities were computed. When ascertainment effort plays a role in interpreting the epidemiological dynamics, it is useful to analyze not only reported (confirmed or suspected) cases, but also the temporal distribution of deceased individuals to avoid any strong impact of time dependent changes in diagnosis and reporting.

Transmission dynamics of cholera in Yemen, 2017: a real time forecasting

BACKGROUND

A large epidemic of cholera, caused by Vibrio cholerae, serotype Ogawa, has been ongoing in Yemen, 2017. To improve the situation awareness, the present study aimed to forecast the cholera epidemic, explicitly addressing the reporting delay and ascertainment bias.

METHODS

Using weekly incidence of suspected cases, updated as a revised epidemic curve every week, the reporting delay was explicitly incorporated into the estimation model. Using the weekly case fatality risk as calculated by the World Health Organization, ascertainment bias was adjusted, enabling us to parameterize the family of logistic curves (i.e., logistic and generalized logistic models) for describing the unbiased incidence in 2017.

RESULTS

The cumulative incidence at the end of the epidemic, was estimated at 790,778 (95% CI: 700,495, 914,442) cases and 767,029 (95% CI: 690,877, 871,671) cases, respectively, by using logistic and generalized logistic models. It was also estimated that we have just passed through the epidemic peak by week 26, 2017. From week 27 onwards, the weekly incidence was predicted to decrease.

CONCLUSIONS

Cholera epidemic in Yemen, 2017 was predicted to soon start to decrease. If the weekly incidence is reported in the up-to-the-minute manner and updated in later weeks, not a single data point but the entire epidemic curve must be precisely updated.

Exploring novel sero-epidemiological tools-Effect of different storage conditions on longitudinal stability of microarray slides comprising influenza A-, measles- and Streptococcus pneumoniae antigens

In this study we evaluated the long-term stability of a microarray-based serological screening platform, containing antigens of influenza A, measles and Streptococcus pneumoniae, as part of a preparedness research program aiming to develop assays for syndromic disease detection. Spotted microarray slides were kept at four different storage regimes with varying temperature and humidity conditions. We showed that under the standard storage condition in a temperature-controlled (21°C) and desiccated environment (0% relative humidity), microarray slides remained stable for at least 22 months without loss of antigen quality, whereas the other three conditions (37°C, desiccated; Room temperature, non-desiccated; Frozen, desiccated) produced acceptable results for some antigens (influenza A, S.pneumoniae), but not for others (measles). We conclude that these arrays for multiplex antibody testing can be prepared and stored for prolonged periods of time, which aids laboratory-preparedness and facilitates sero-epidemiological studies.

Optimizing the precision of case fatality ratio estimates under the surveillance pyramid approach

In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the probability of death after developing symptoms—the symptomatic case fatality ratio (sCFR)—is essential. Estimation of the sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally, we review the equally important issue of accuracy.

Dynamics of a new strain of the H1N1 influenza A virus incorporating the effects of repetitive contacts

The respiratory disease caused by the Influenza A Virus is occurring worldwide. The transmission for new strain of the H1N1 Influenza A virus is studied by formulating a SEIQR (susceptible, exposed, infected, quarantine, and recovered) model to describe its spread. In the present model, we have assumed that a fraction of the infected population will die from the disease. This changes the mathematical equations governing the transmission. The effect of repetitive contact is also included in the model. Analysis of the model by using standard dynamical modeling method is given. Conditions for the stability of equilibrium state are given. Numerical solutions are presented for different values of parameters. It is found that increasing the amount of repetitive contacts leads to a decrease in the peak numbers of exposed and infectious humans. A stability analysis shows that the solutions are robust.

Early detection of nosocomial outbreaks caused by rare pathogens: a case study employing score prediction interval

Objectives

Nosocomial outbreaks involve only a small number of cases and limited baseline data. The present study proposes a method to detect the nosocomial outbreaks caused by rare pathogens, exploiting score prediction interval of a Poisson distribution.

Methods

The proposed method was applied to three empirical datasets of nosocomial outbreaks in Japan: outbreaks of (1) multidrug-resistant Acinetobacter baumannii (n = 46) from 2009 to 2010, (2) multidrug-resistant Pseudomonas aerginosa (n = 18) from 2009 to 2010, and (3) Serratia marcescens (n = 226) from 1999 to 2000.

Results

The proposed method successfully detected all three outbreaks during the first 2 months. Both the model-based and empirically derived threshold values indicated that the nosocomial outbreak of rare infectious disease may be declared upon diagnosis of index case(s), although the sensitivity and specificity were highly variable.

Conclusion

The findings support the practical notion that, upon diagnosis of index patient(s), one should immediately start the outbreak investigation of nosocomial outbreak caused by a rare pathogen. The proposed score prediction interval can permit easy computation of outbreak threshold in hospital settings among healthcare experts.

Pandemic controllability: a concept to guide a proportionate and flexible operational response to future influenza pandemics

The 2009 H1N1 influenza pandemic posed challenges for governments worldwide. Strategies designed to limit community transmission, such as antiviral deployment, were largely ineffective due to both feasibility constraints and the generally mild nature of disease, resulting in incomplete case ascertainment. Reviews of national pandemic plans have identified pandemic impact, primarily linked to measures of transmissibility and severity, as a key concept to incorporate into the next generation of plans. While an assessment of impact provides the rationale under which interventions may be warranted, it does not directly provide an assessment on whether particular interventions may be effective. Such considerations motivate our introduction of the concept of pandemic controllability. For case-targeted interventions, such as antiviral treatment and post-exposure prophylaxis, we identify the visibility and transmissibility of a pandemic as the key drivers of controllability. Taking a case-study approach, we suggest that high-impact pandemics, for which control is most desirable, are likely uncontrollable with case-targeted interventions. Strategies that do not rely on the identification of cases may prove relatively more effective. By introducing a pragmatic framework for relating the assessment of impact to the ability to mitigate an epidemic (controllability), we hope to address a present omission identified in pandemic response plans.

Participation of ICUs in critical care pandemic research: a province wide, cross-sectional survey

OBJECTIVE

Little information exists to identify barriers to participation in pandemic research involving critically ill patients. We sought to characterize clinical research activity during the recent influenza A pandemic and to understand the experiences, beliefs, and practices of key stakeholders involved in pandemic research implementation.

DESIGN

Cross-sectional, provincial postal questionnaire.

SETTING

Level III ICUs.

PARTICIPANTS

ICU administrators and research coordinators.

MEASUREMENTS

We used rigorous survey methodology to identify potential respondents and to develop, test, and administer two-related questionnaires.

MAIN RESULTS

We analyzed responses from 39 research coordinators and 139 administrators (response rates: 70.9% and 73.2%, respectively). Compared with non-influenza A studies, influenza A studies were less likely to be randomized trials and most often investigator-initiated and peer-review funded. Whereas both respondent groups felt that pandemic research would be helpful in providing care during future pandemics, research coordinators placed significantly greater importance on their ICU's participation in pandemic research. Both respondent groups expressed a need for rapid approval processes, designated funding for research personnel, adequate funding for start-up and patient screening, preapproved template protocols and consent forms, and clearer guidance regarding co-enrollment. Research coordinators acknowledged a need for alternative consent models to increase their capacity to participate in future pandemic research. More administrators expressed willingness to participate in the next pandemic if the required research resources were made available to them.

CONCLUSIONS

Whereas research personnel and administrators support participation in pandemic ICU research, several modifiable barriers to participation exist. Pandemic research preparedness planning with regulatory bodies and dedicated funding to support research infrastructure, especially in community settings, are required to optimize future pandemic research participation.

Age-dependent estimates of the epidemiological impact of pandemic influenza (H1N1-2009) in Japan

The total number of influenza cases with medical attendance has been estimated from sentinel surveillance data in Japan under a random sampling assumption of sentinel medical institutions among the total medical institutions. The 2009 pandemic offered a research opportunity to validate the sentinel-based estimation method using the estimated proportion of infections measured by the population-wide seroepidemiological survey employing hemagglutinin inhibition (HI) assay. For the entire population, we estimated the age-standardized proportion of infections at 28.5% and 23.5% using cut-off values of HI titer at 1 : 20 and 1 : 40, respectively. Investigating the age profiles, we show that the estimated influenza-like illness (ILI) cases with medical attendance exceeded the estimated infections among those aged from 0 to 19 years, indicating an overestimation of the magnitude by sentinel-based estimation method. The ratio of estimated cases to estimated infections decreased as a function of age. Examining the geographic distributions, no positive correlation was identified between the estimated cases and infections. Our findings indicate a serious technical limitation of the so-called multiplier method in appropriately quantifying the risk of influenza due to limited specificity of ILI and reporting bias. A seroepidemiological study should be planned in advance of a pandemic.

Effectiveness of antiviral prophylaxis coupled with contact tracing in reducing the transmission of the influenza A (H1N1-2009): a systematic review

BACKGROUND

During the very early stage of the 2009 pandemic, mass chemoprophylaxis was implemented as part of containment measure. The purposes of the present study were to systematically review the retrospective studies that investigated the effectiveness of antiviral prophylaxis during the 2009 pandemic, and to explicitly estimate the effectiveness by employing a mathematical model.

METHODS

A systematic review identified 17 articles that clearly defined the cases and identified exposed individuals based on contact tracing. Analysing a specific school-driven outbreak, we estimated the effectiveness of antiviral prophylaxis using a renewal equation model. Other parameters, including the reproduction number and the effectiveness of antiviral treatment and school closure, were jointly estimated.

RESULTS

Based on the systematic review, median secondary infection risks (SIRs) among exposed individuals with and without prophylaxis were estimated at 2.1% (quartile: 0, 12.2) and 16.6% (quartile: 8.4, 32.4), respectively. A very high heterogeneity in the SIR was identified with an estimated I2 statistic at 71.8%. From the outbreak data in Madagascar, the effectiveness of mass chemoprophylaxis in reducing secondary transmissions was estimated to range from 92.8% to 95.4% according to different model assumptions and likelihood functions, not varying substantially as compared to other parameters.

CONCLUSIONS

Only based on the meta-analysis of retrospective studies with different study designs and exposure settings, it was not feasible to estimate the effectiveness of antiviral prophylaxis in reducing transmission. However, modelling analysis of a single outbreak successfully yielded an estimate of the effectiveness that appeared to be robust to model assumptions. Future studies should fill the data gap that has existed in observational studies and allow mathematical models to be used for the analysis of meta-data.

The relationship between tuberculosis and influenza death during the influenza (H1N1) pandemic from 1918-19

The epidemiological mechanisms behind the W-shaped age-specific influenza mortality during the Spanish influenza (H1N1) pandemic 1918-19 have yet to be fully clarified. The present study aimed to develop a formal hypothesis: tuberculosis (TB) was associated with the W-shaped influenza mortality from 1918-19. Three pieces of epidemiological information were assessed: (i) the epidemic records containing the age-specific numbers of cases and deaths of influenza from 1918-19, (ii) an outbreak record of influenza in a Swiss TB sanatorium during the pandemic, and (iii) the age-dependent TB mortality over time in the early 20th century. Analyzing the data (i), we found that the W-shaped pattern was not only seen in mortality but also in the age-specific case fatality ratio, suggesting the presence of underlying age-specific risk factor(s) of influenza death among young adults. From the data (ii), TB was shown to be associated with influenza death (P = 0.09), and there was no influenza death among non-TB controls. The data (iii) were analyzed by employing the age-period-cohort model, revealing harvesting effect in the period function of TB mortality shortly after the 1918-19 pandemic. These findings suggest that it is worthwhile to further explore the role of TB in characterizing the age-specific risk of influenza death.

Real-time investigation of measles epidemics with estimate of vaccine efficacy

As part of measles elimination effort, evaluation of the vaccination program and real-time assessment of the epidemic dynamics constitute two important tasks to improve and strengthen the control. The present study aimed to develop an epidemiological modeling method which can be applied to estimating the vaccine efficacy at an individual level while conducting the timely investigation of the epidemic. The multivariate renewal process model was employed to describe the temporal evolution of infection by vaccination history, jointly estimating the time-dependent reproduction number and the vaccine efficacy. Analyzing the enhanced surveillance data of measles in Aichi prefecture, Japan from 2007-08, the vaccine efficacy was estimated at 96.7% (95% confidence interval: 95.8, 97.4). Using an age structured model, the vaccine efficacy among those aged from 5-19 years was shown to be smaller than that among those from 0-4 years. The age-dependent vaccine efficacy estimate informs the age-groups to be targeted for revaccination. Because the estimation method can rest on readily available epidemiological data, the proposed model has a potential to be integrated with routine surveillance.