Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Using Phenomenological Models to Characterize Transmissibility and Forecast Patterns and Final Burden of Zika Epidemics

Background: The World Health Organization declared the ongoing Zika virus (ZIKV) epidemic in the Americas a Public Health Emergency of International Concern on February 1, 2016. ZIKV disease in humans is characterized by a “dengue-like” syndrome including febrile illness and rash. However, ZIKV infection in early pregnancy has been associated with severe birth defects, including microcephaly and other developmental issues. Mechanistic models of disease transmission can be used to forecast trajectories and likely disease burden but are currently hampered by substantial uncertainty on the epidemiology of the disease (e.g., the role of asymptomatic transmission, generation interval, incubation period, and key drivers). When insight is limited, phenomenological models provide a starting point for estimation of key transmission parameters, such as the reproduction number, and forecasts of epidemic impact.

Methods: We obtained daily counts of suspected Zika cases by date of symptoms onset from the Secretary of Health of Antioquia, Colombia during January-April 2016. We calibrated the generalized Richards model, a phenomenological model that accommodates a variety of early exponential and sub-exponential growth kinetics, against the early epidemic trajectory and generated predictions of epidemic size. The reproduction number was estimated by applying the renewal equation to incident cases simulated from the fitted generalized-growth model and assuming gamma or exponentially-distributed generation intervals derived from the literature. We estimated the reproduction number for an increasing duration of the epidemic growth phase.

Results: The reproduction number rapidly declined from 10.3 (95% CI: 8.3, 12.4) in the first disease generation to 2.2 (95% CI: 1.9, 2.8) in the second disease generation, assuming a gamma-distributed generation interval with the mean of 14 days and standard deviation of 2 days. The generalized-Richards model outperformed the logistic growth model and provided forecasts within 22% of the actual epidemic size based on an assessment 30 days into the epidemic, with the epidemic peaking on day 36.

Conclusion: Phenomenological models represent promising tools to generate early forecasts of epidemic impact particularly in the context of substantial uncertainty in epidemiological parameters. Our findings underscore the need to treat the reproduction number as a dynamic quantity even during the early growth phase, and emphasize the sensitivity of reproduction number estimates to assumptions on the generation interval distribution.

Using Surveillance Data to Identify Risk Factors for Severe H1N1 in First Nations

Objective

We sought to measure from surveillance data the effect of proximity to an urban centre (rurality) and other risk factors, (e.g., age, residency on a FN reservation, and pandemic wave) on hospitalization and intensive care unit admission for severe influenza.

Introduction

Research has shown that Canadian First Nation (FN) populations were disproportionately affected by the 2009 H1N1 influenza pandemic. However, the mechanisms for the disproportionate outcomes are not well understood. Possibilities such as healthcare access, infrastructure and housing issues, and pre-existing comorbidities have been suggested. We estimated the odds of hospitalization and intensive care unit admission for cases of H1N1 influenza among FN living in Manitoba, Canada, to determine the effect of location of residency and other factors on disease outcomes during the 2009 H1N1 pandemic.

Methods

We obtained surveillance data on laboratory confirmed cases of pandemic H1N1 influenza from the province of Manitoba. These data described demographic characteristics, residence location, and dates of hospital and ICU admission. We measured the rurality of each case using a pre-exiting scale (Rambeau & Todd, 2000). We tabulated the number of hospitalizations (and ICU admissions) stratified first by reservation residency and second by rurality and calculated unadjusted odds ratios. We then used logistic regression to calculate the odds of hospitalization given infection (and the odds of ICU admission given hospitalization), adjusting for age, reservation residency, rurality, and pandemic wave. We also investigated the effect of rurality and reserve residency on time to hospitalization from infection.

Results

FN individuals diagnosed with influenza and living on-reserve were more likely to be hospitalized than those living off-reserve, even after controlling for the effects of rurality (OR: 2.16, 95% CI: 1.15, 4.05). FN living in rural areas were hospitalized more frequently and experienced longer delays between infection and hospitalization than FN residing in more urban areas. Rurality and reserve residency had less effect on ICU admissions once an individual was hospitalized.

Conclusions

While it is established that FN individuals had disproportionately high rates of severe outcomes from H1N1, the causal mechanisms at work are not well understood. Reasonable possibilities include barriers to healthcare access, lack of proper housing and infrastructure, and pre-existing comorbidities. This research using surveillance data suggests that geographic location has an effect on healthcare access, including both on vs. off reserve residency as well as rurality.

Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza

BACKGROUND

In the face of an influenza pandemic, accurate estimates of epidemiologic parameters are required to help guide decision-making. We sought to estimate epidemiologic parameters for pandemic H1N1 influenza using data from initial reports of laboratory-confirmed cases.

METHODS

We obtained data on laboratory-confirmed cases of pandemic H1N1 influenza reported in the province of Ontario, Canada, with dates of symptom onset between Apr. 13 and June 20, 2009. Incubation periods and duration of symptoms were estimated and fit to parametric distributions. We used competing-risk models to estimate risk of hospital admission and case-fatality rates. We used a Markov Chain Monte Carlo model to simulate disease transmission.

RESULTS

The median incubation period was 4 days and the duration of symptoms was 7 days. Recovery was faster among patients less than 18 years old than among older patients (hazard ratio 1.23, 95% confidence interval 1.06-1.44). The risk of hospital admission was 4.5% (95% CI 3.8%-5.2%) and the case-fatality rate was 0.3% (95% CI 0.1%-0.5%). The risk of hospital admission was highest among patients less than 1 year old and those 65 years or older. Adults more than 50 years old comprised 7% of cases but accounted for 7 of 10 initial deaths (odds ratio 28.6, 95% confidence interval 7.3-111.2). From the simulation models, we estimated the following values (and 95% credible intervals): a mean basic reproductive number (R0, the number of new cases created by a single primary case in a susceptible population) of 1.31 (1.25-1.38), a mean latent period of 2.62 (2.28-3.12) days and a mean duration of infectiousness of 3.38 (2.06-4.69) days. From these values we estimated a serial interval (the average time from onset of infectiousness in a case to the onset of infectiousness in a person infected by that case) of 4-5 days.

INTERPRETATION

The low estimates for R0 indicate that effective mitigation strategies may reduce the final epidemic impact of pandemic H1N1 influenza.