Abstract:During an epidemic, real-time estimation of the effective reproduction number supports decision makers to introduce timely and effective public health measures. We estimate the time-varying effective reproduction number, Rt, during Aotearoa New Zealand’s August 2021 outbreak of the Delta variant of SARS-CoV-2, by fitting the publicly available EpiNow2 model to New Zealand case data. While we do not explicitly model non-pharmaceutical interventions or vaccination coverage, these two factors were the leading dri… Show more
“…This highlights the potential impact of model (mis)specification or a poorly sampled parameter space on model-based projections and estimates. Despite these differences, however, the model reproduction number followed a reasonably similar trajectory to that estimated by EpiNow2 using daily cases up to 26 January 2022 45 and the 90% credible bands overlapped for the duration of the simulation.…”
Section: Resultsmentioning
confidence: 67%
“… Figure 4 Model results fitted to data up to 28 November 2021. ( a ) Inferred control function ; ( b ) Effective reproduction number calculated in the model (blue) based on time-varying control function , and vaccine-derived and infection-derived immunity, and independently estimated using the EpiNow2 package 23 , 45 (median estimate red solid, 90% credible band red dashed); ( c ) Daily reported cases; ( d ) New daily hospital admission; ( e ) Cumulative deaths; ( f ) Hospital beds occupied. In ( c ), green points show data that was used to fit the control function shown in ( a ).…”
Section: Resultsmentioning
confidence: 99%
“…Dashed vertical lines show the times of key changes to public health restrictions for the Auckland region (see Table 1). the nowcasting package EpiNow2 23,45 appears more sensitive to slight variations in cases and shows a small corresponding increase in R eff (t) after the shift to CPF red and a further increase after the shift to CPF orange. This highlights the potential impact of model (mis)specification or a poorly sampled parameter space on model-based projections and R eff (t) estimates.…”
Epidemiological models range in complexity from relatively simple statistical models that make minimal assumptions about the variables driving epidemic dynamics to more mechanistic models that include effects such as vaccine-derived and infection-derived immunity, population structure and heterogeneity. The former are often fitted to data in real-time and used for short-term forecasting, while the latter are more suitable for comparing longer-term scenarios under differing assumptions about control measures or other factors. Here, we present a mechanistic model of intermediate complexity that can be fitted to data in real-time but is also suitable for investigating longer-term dynamics. Our approach provides a bridge between primarily empirical approaches to forecasting and assumption-driven scenario models. The model was developed as a policy advice tool for New Zealand’s 2021 outbreak of the Delta variant of SARS-CoV-2 and includes the effects of age structure, non-pharmaceutical interventions, and the ongoing vaccine rollout occurring during the time period studied. We use an approximate Bayesian computation approach to infer the time-varying transmission coefficient from real-time data on reported cases. We then compare projections of the model with future, out-of-sample data. We find that this approach produces a good fit with in-sample data and reasonable forward projections given the inherent limitations of predicting epidemic dynamics during periods of rapidly changing policy and behaviour. Results from the model helped inform the New Zealand Government’s policy response throughout the outbreak.
“…This highlights the potential impact of model (mis)specification or a poorly sampled parameter space on model-based projections and estimates. Despite these differences, however, the model reproduction number followed a reasonably similar trajectory to that estimated by EpiNow2 using daily cases up to 26 January 2022 45 and the 90% credible bands overlapped for the duration of the simulation.…”
Section: Resultsmentioning
confidence: 67%
“… Figure 4 Model results fitted to data up to 28 November 2021. ( a ) Inferred control function ; ( b ) Effective reproduction number calculated in the model (blue) based on time-varying control function , and vaccine-derived and infection-derived immunity, and independently estimated using the EpiNow2 package 23 , 45 (median estimate red solid, 90% credible band red dashed); ( c ) Daily reported cases; ( d ) New daily hospital admission; ( e ) Cumulative deaths; ( f ) Hospital beds occupied. In ( c ), green points show data that was used to fit the control function shown in ( a ).…”
Section: Resultsmentioning
confidence: 99%
“…Dashed vertical lines show the times of key changes to public health restrictions for the Auckland region (see Table 1). the nowcasting package EpiNow2 23,45 appears more sensitive to slight variations in cases and shows a small corresponding increase in R eff (t) after the shift to CPF red and a further increase after the shift to CPF orange. This highlights the potential impact of model (mis)specification or a poorly sampled parameter space on model-based projections and R eff (t) estimates.…”
Epidemiological models range in complexity from relatively simple statistical models that make minimal assumptions about the variables driving epidemic dynamics to more mechanistic models that include effects such as vaccine-derived and infection-derived immunity, population structure and heterogeneity. The former are often fitted to data in real-time and used for short-term forecasting, while the latter are more suitable for comparing longer-term scenarios under differing assumptions about control measures or other factors. Here, we present a mechanistic model of intermediate complexity that can be fitted to data in real-time but is also suitable for investigating longer-term dynamics. Our approach provides a bridge between primarily empirical approaches to forecasting and assumption-driven scenario models. The model was developed as a policy advice tool for New Zealand’s 2021 outbreak of the Delta variant of SARS-CoV-2 and includes the effects of age structure, non-pharmaceutical interventions, and the ongoing vaccine rollout occurring during the time period studied. We use an approximate Bayesian computation approach to infer the time-varying transmission coefficient from real-time data on reported cases. We then compare projections of the model with future, out-of-sample data. We find that this approach produces a good fit with in-sample data and reasonable forward projections given the inherent limitations of predicting epidemic dynamics during periods of rapidly changing policy and behaviour. Results from the model helped inform the New Zealand Government’s policy response throughout the outbreak.
“…Some MCMC convergence issues were resolved by reducing the parameter fitting period (e.g., truncating the beginning of the time series). One study reported that EpiNow2 estimates are more reliable when case numbers at each time step are large and there are at least 14 timepoints without zeroes (Binny et al, 2022).…”
Section: W I T H D R a W N S E E M A N U S C R I P T D O I F O R D E ...mentioning
Mpox is a zoonotic disease endemic in Central and West Africa. In May 2022, an outbreak of mpox characterized by human-to-human transmission was detected in multiple non-endemic countries. We performed nowcasting and forecasting for the 2022 mpox outbreak in the United States using the R package EpiNow2. We generated nowcasts/forecasts at the national level, by Census region, and for jurisdictions reporting the greatest number of mpox cases. Modeling results were shared for situational awareness within the Centers for Disease Control and Prevention (CDC) Mpox Response and publicly on the CDC website. We retrospectively evaluated forecast predictions at four key phases during the outbreak using three metrics, the weighted interval score, mean absolute error, and prediction interval coverage. We compared the performance of EpiNow2 with a naive Bayesian generalized linear model (GLM). The EpiNow2 model had less probabilistic error than the GLM during every outbreak phase except for the early phase. We share our experiences with an existing tool for nowcasting/forecasting and highlight areas of improvement for the development of future tools. We also reflect on lessons learned regarding data quality issues and adapting modeling results for different audiences.
“…An ABC method was developed for inferring the value of the time-varying “control function” in equation (2.2) from data on new daily cases [67]. Real-time estimation of was also provided using the EpiNow2 package, which uses a semi-mechanistic model based on the renewal equation, equation (2.1) [1, 8]. This was suitable for short-term forecasts.…”
Section: Modelling Tools Used In Australia and New Zealandmentioning
Mathematical modelling has been used to support the response to the COVID-19 pandemic in countries around the world including Australia and New Zealand. Both these countries have followed similar pandemic response strategies, using a combination of strict border measures and community interventions to minimize infection rates until high vaccine coverage was achieved. This required a different set of modelling tools to those used in countries that experienced much higher levels of prevalence throughout the pandemic.
In this article, we provide an overview of some of the mathematical modelling and data analytics work that has helped to inform the policy response to the pandemic in Australia and New Zealand. This is a reflection on our experiences working at the modelling–policy interface and the impact this has had on the pandemic response. We outline the various types of model outputs, from short-term forecasts to longer-term scenario models, that have been used in different contexts. We discuss issues relating to communication between mathematical modellers and stakeholders such as health officials and policymakers. We conclude with some future challenges and opportunities in this area.
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