Summary
There is currently no reliable model that compiles global COVID-19 information, including transmission rates, travel policies and developing treatments, to predict when the pandemic will end [1]. A systematic review of 51 studies describing 66 prediction models included models for predicting hospital admission, diagnostic models for detecting COVID-19 and prognostic models for predicting mortality risk, progression to severe disease and length of hospital stay [1]. It was concluded that proposed models are poorly reported, had high risk of bias, and their reported performance is likely optimistic [1]. In order to develop more rigorous prediction models and validate promising ones, immediate sharing of well-documented individual data from COVID-19 studies are needed [1].
Disease modelling has played a key role in providing information to inform decision-making by governments and stakeholders for managing this pandemic. Modelling is based on detailed information about population size and density, age distribution, health care resources, public health measures and disease transmission [4]. Characteristics of a new virus including its immunology and transmission will become better understood over time. Effective social restrictions and enforcing health behaviours in a population will help minimize disease transmission [2]. Effects of policy implementation are best understood after their adoption, when monitoring disease spread [2]. So far, models that exist attempt to predict outcomes of this current first wave in a specific country or globally. However, models are only as reliable as the scientific information they rely on, and current data are limited [3]. Comprehensive mathematical models that account for complex viruses and society-based variables often require months to years [7]. Pandemics end if and when herd immunity in the population is acquired (via vaccines or natural disease spread throughout the population), or through the eradication of the disease, likely with the help of public health measures [5]. If insufficient herd immunity is developed, subsequent waves may occur, and its dynamics throughout the population cannot be predicted until that time. Additionally, different regions will have different policies, practices and transmission dynamics and it is unlikely that a single model is able to aggregate worldwide COVID-19 data where there are variations in measurement and reporting as well as reliability, and then make an accurate prediction regarding the end of the pandemic that applies to one or several jurisdictions [6].
Contingencies
The development of a detailed, robust answer relies on several factors. To provide a detailed REAL Note requires knowing the following:
o Sufficient and accurate data on infection rates, immunity and transmission dynamics
o Knowing when a vaccine and/or treatment will become available, its effectiveness and how accessible it will be to all populations
o Current assumptions regarding dynamic physical distancing and other public health measures that are projected into the future
o Canadian and international travel policies
Evidence
Review of Evidence
| Resource | Type/Source of Evidence | Last Updated |
|---|---|---|
|
Modelling the Pandemic: The simulations driving the world’s response to COVID-19 — Adam David |
Report | Last Updated: April 15, 2020 |
|
Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis — Fang et al. |
Systematic Review | Last Updated: March 15, 2020 |
|
Mathematical modelling and prediction in infectious disease epidemiology — A. Huppert & G. Katriel |
Systematic Review | Last Updated: October 31, 2013 |
|
New coronavirus outbreak: Framing questions for pandemic prevention — Layne et al. |
Commentary | Last Updated: March 10, 2020 |
|
Herd Immunity: History, Theory, Practice — Paul E. M. Fine |
Rapid Review | Last Updated: December 31, 1992 |
|
Caution Warranted: Using the Institute for Health Metrics and Evaluation Model for Predicting the Course of the COVID-19 Pandemic — Jewell et al. |
Single Study | Last Updated: April 13, 2020 |
|
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal — Wynants et al. |
Systematic Review | Last Updated: April 6, 2020 |
| Disclaimer: The summaries provided are distillations of reviews that have synthesized many individual studies. As such, summarized information may not always be applicable to every context. Each piece of evidence is hyperlinked to the original source. | ||