key questions for modelling covid 19 exit strategies CORD-Papers-2022-06-02 (Version 1)

Title: Key questions for modelling COVID-19 exit strategies
Abstract: Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here we report discussions from the Isaac Newton Institute Models for an exit strategy workshop (1115 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that if answered would allow for more accurate predictions of the effects of different exit strategies. Based on these questions we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.
Published: 2020-08-12
Journal: Proc Biol Sci
DOI: 10.1098/rspb.2020.1405
DOI_URL: http://doi.org/10.1098/rspb.2020.1405
Author Name: Thompson Robin N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/thompson_robin_n
Author Name: Hollingsworth T Dirdre
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hollingsworth_t_dirdre
Author Name: Isham Valerie
Author link: https://covid19-data.nist.gov/pid/rest/local/author/isham_valerie
Author Name: Arribas Bel Daniel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/arribas_bel_daniel
Author Name: Ashby Ben
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ashby_ben
Author Name: Britton Tom
Author link: https://covid19-data.nist.gov/pid/rest/local/author/britton_tom
Author Name: Challenor Peter
Author link: https://covid19-data.nist.gov/pid/rest/local/author/challenor_peter
Author Name: Chappell Lauren H K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chappell_lauren_h_k
Author Name: Clapham Hannah
Author link: https://covid19-data.nist.gov/pid/rest/local/author/clapham_hannah
Author Name: Cunniffe Nik J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cunniffe_nik_j
Author Name: Dawid A Philip
Author link: https://covid19-data.nist.gov/pid/rest/local/author/dawid_a_philip
Author Name: Donnelly Christl A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/donnelly_christl_a
Author Name: Eggo Rosalind M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/eggo_rosalind_m
Author Name: Funk Sebastian
Author link: https://covid19-data.nist.gov/pid/rest/local/author/funk_sebastian
Author Name: Gilbert Nigel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gilbert_nigel
Author Name: Glendinning Paul
Author link: https://covid19-data.nist.gov/pid/rest/local/author/glendinning_paul
Author Name: Gog Julia R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gog_julia_r
Author Name: Hart William S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hart_william_s
Author Name: Heesterbeek Hans
Author link: https://covid19-data.nist.gov/pid/rest/local/author/heesterbeek_hans
Author Name: House Thomas
Author link: https://covid19-data.nist.gov/pid/rest/local/author/house_thomas
Author Name: Keeling Matt
Author link: https://covid19-data.nist.gov/pid/rest/local/author/keeling_matt
Author Name: Kiss Istvn Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kiss_istvn_z
Author Name: Kretzschmar Mirjam E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kretzschmar_mirjam_e
Author Name: Lloyd Alun L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lloyd_alun_l
Author Name: McBryde Emma S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mcbryde_emma_s
Author Name: McCaw James M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mccaw_james_m
Author Name: McKinley Trevelyan J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mckinley_trevelyan_j
Author Name: Miller Joel C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/miller_joel_c
Author Name: Morris Martina
Author link: https://covid19-data.nist.gov/pid/rest/local/author/morris_martina
Author Name: O aposNeill Philip D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/o_aposneill_philip_d
Author Name: Parag Kris V
Author link: https://covid19-data.nist.gov/pid/rest/local/author/parag_kris_v
Author Name: Pearson Carl A B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pearson_carl_a_b
Author Name: Pellis Lorenzo
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pellis_lorenzo
Author Name: Pulliam Juliet R C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pulliam_juliet_r_c
Author Name: Ross Joshua V
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ross_joshua_v
Author Name: Tomba Gianpaolo Scalia
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tomba_gianpaolo_scalia
Author Name: Silverman Bernard W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/silverman_bernard_w
Author Name: Struchiner Claudio J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/struchiner_claudio_j
Author Name: Tildesley Michael J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tildesley_michael_j
Author Name: Trapman Pieter
Author link: https://covid19-data.nist.gov/pid/rest/local/author/trapman_pieter
Author Name: Webb Cerian R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/webb_cerian_r
Author Name: Mollison Denis
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mollison_denis
Author Name: Restif Olivier
Author link: https://covid19-data.nist.gov/pid/rest/local/author/restif_olivier
sha: f24c25631abd70dfdccec154121a7bafeba65319
license: cc-by
license_url: https://creativecommons.org/licenses/by/4.0/
source_x: Medline; PMC
source_x_url: https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 32781946
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/32781946
pmcid: PMC7575516
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575516
url: https://doi.org/10.1098/rspb.2020.1405 https://www.ncbi.nlm.nih.gov/pubmed/32781946/
has_full_text: TRUE
Keywords Extracted from Text Content: SARS-CoV-2 lockdowns royalsocietypublishing.org/journal/rspb IgG. characteristics-notably human contact network IgM contacts Wuhan (China Pandemics near-neighbour contacts [1] [2] [3] test-trace-isolate post-lockdown immunoglobulin M foot IgG herd children Congo particularly-but §4a-c COVID-19 [7, 12] [ Figure 1 [116] hosts ERA5 SARS-CoV-2 [41 §3 Tobago [127 restrictions-for networks t. [111] [158] [159] [160] super-spreaders LMICs between-household COVID-19 lockdowns Cori lockdown [7 eradicated-is timeuse UK immunoglobulin G rough-and-ready people lockdowns out-of-household measles super-spreaders [99 body Herd nodes NPIs hubs participants p(t coronavirus 2 [123] policymakers-particularly ABM coronavirus disease 2019 Infectious R(t 2.5-4 Sweden polio Lockdown [4, 5] contact networks [161, 162] individuals Post-lockdown epidemic- §2d SARS-CoV-2 lockdown www.newton.ac.uk Pandemics
Extracted Text Content in Record: First 5000 Characters:OR, 0000-0001-9158-853X Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-tomiddle-income countries. This will provide important information for planning exit strategies that balance socioeconomic benefits with public health. As of 3 August 2020, the coronavirus disease 2019 (COVID- 19) pandemic has been responsible for more than 18 million reported cases worldwide, including over 692 000 deaths. Mathematical modelling is playing an important role in guiding interventions to reduce the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the impact of the virus has varied significantly across the world, and different countries have taken different approaches to counter the pandemic, many national governments introduced packages of intense non-pharmaceutical interventions (NPIs), informally known as 'lockdowns'. Although the socio-economic costs (e.g. job losses and long-term mental health effects) are yet to be assessed fully, public health measures have led to substantial reductions in transmission [1] [2] [3] . Data from countries such as Sweden and Japan, where epidemic waves peaked without strict lockdowns, will be useful for comparing approaches and conducting retrospective cost-benefit analyses. As case numbers have either stabilized or declined in many countries, attention has turned to strategies that allow restrictions to be lifted [4, 5] in order to alleviate the economic, social and other health costs of lockdowns. However, in countries with active transmission still occurring, daily disease incidence could increase again quickly, while countries that have suppressed community transmission face the risk of transmission reestablishing due to reintroductions. In the absence of a vaccine or sufficient herd immunity to reduce transmission substantially, COVID-19 exit strategies pose unprecedented challenges to policymakers and the scientific community. Given our limited knowledge, and the fact that entire packages of interventions were often introduced in quick succession as case numbers increased, it is challenging to estimate the effects of removing individual measures directly and modelling remains of paramount importance. We report discussions from the 'Models for an exit strategy' workshop (11) (12) (13) (14) (15) May 2020) that took place online as part of the Isaac Newton Institute's 'Infectious Dynamics of Pandemics' programme. We outline progress to date and open questions in modelling exit strategies that arose during discussions at the workshop. Most participants were working actively on COVID-19 at the time of the workshop, often with the aim of providing evidence to governments, public health authorities and the general public to support the pandemic response. After four months of intense model development and data analysis, the workshop gave participants a chance to take stock and openly share their views of the main challenges they are facing. A range of countries was represented, providing a unique forum to discuss the different epidemic dynamics and policies around the world. Although the main focus was on epidemiological models, the interplay with other disciplines formed an integral part of the discussion. The purpose of this article is twofold: to highlight key knowledge gaps hindering current predictions and projections, and to provide a roadmap for modellers and other scientists towards solutions. Given that SARS-CoV-2 is a newly discovered virus, the evidence base is changing rapidly. To conduct a systematic review, we asked the large group of researchers at the workshop for their expert opinions on the most important open questions, and relevant literature, that will enable exit strategies to be planned with more precision. By inviting contributions from representatives of different count
Keywords Extracted from PMC Text: SARS-CoV-2 [41,42 coronavirus disease 2019 IgG body twin' Cori p(t IgM UK 2.5–4 super-spreaders [99,100] lockdowns immunoglobulin G LMICs polio between-household [116] Sweden Pandemics [16] contact network [111] i(t)>1−1/R0 people NPIs out-of-household hubs Richardson–Lucy lockdown Herd 47,59 children epidemic—§2d COVID-19 lockdowns near-neighbour contacts hosts ABM ERA5 contacts age-structures R(t 81,82 herd Wallinga– t. Lockdown nodes people's individuals participants Infectious [123] human foot super-spreaders Tobago [127 COVID-19 73,74 SARS-CoV-2 [118,119] immunoglobulin M lockdown [7 measles Wuhan (China §3 rough-and-ready [120,121] IgG. Congo Post-lockdown networks
Extracted PMC Text Content in Record: First 5000 Characters:As of 3 August 2020, the coronavirus disease 2019 (COVID-19) pandemic has been responsible for more than 18 million reported cases worldwide, including over 692 000 deaths. Mathematical modelling is playing an important role in guiding interventions to reduce the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the impact of the virus has varied significantly across the world, and different countries have taken different approaches to counter the pandemic, many national governments introduced packages of intense non-pharmaceutical interventions (NPIs), informally known as 'lockdowns'. Although the socio-economic costs (e.g. job losses and long-term mental health effects) are yet to be assessed fully, public health measures have led to substantial reductions in transmission [1–3]. Data from countries such as Sweden and Japan, where epidemic waves peaked without strict lockdowns, will be useful for comparing approaches and conducting retrospective cost–benefit analyses. As case numbers have either stabilized or declined in many countries, attention has turned to strategies that allow restrictions to be lifted [4,5] in order to alleviate the economic, social and other health costs of lockdowns. However, in countries with active transmission still occurring, daily disease incidence could increase again quickly, while countries that have suppressed community transmission face the risk of transmission reestablishing due to reintroductions. In the absence of a vaccine or sufficient herd immunity to reduce transmission substantially, COVID-19 exit strategies pose unprecedented challenges to policymakers and the scientific community. Given our limited knowledge, and the fact that entire packages of interventions were often introduced in quick succession as case numbers increased, it is challenging to estimate the effects of removing individual measures directly and modelling remains of paramount importance. We report discussions from the 'Models for an exit strategy' workshop (11–15 May 2020) that took place online as part of the Isaac Newton Institute's 'Infectious Dynamics of Pandemics' programme. We outline progress to date and open questions in modelling exit strategies that arose during discussions at the workshop. Most participants were working actively on COVID-19 at the time of the workshop, often with the aim of providing evidence to governments, public health authorities and the general public to support the pandemic response. After four months of intense model development and data analysis, the workshop gave participants a chance to take stock and openly share their views of the main challenges they are facing. A range of countries was represented, providing a unique forum to discuss the different epidemic dynamics and policies around the world. Although the main focus was on epidemiological models, the interplay with other disciplines formed an integral part of the discussion. The purpose of this article is twofold: to highlight key knowledge gaps hindering current predictions and projections, and to provide a roadmap for modellers and other scientists towards solutions. Given that SARS-CoV-2 is a newly discovered virus, the evidence base is changing rapidly. To conduct a systematic review, we asked the large group of researchers at the workshop for their expert opinions on the most important open questions, and relevant literature, that will enable exit strategies to be planned with more precision. By inviting contributions from representatives of different countries and areas of expertise (including social scientists, immunologists, epidemic modellers and others), and discussing the expert views raised at the workshop in detail, we sought to reduce geographical and disciplinary biases. All evidence is summarized here in a policy-neutral manner. The questions in this article have been grouped as follows. First, we discuss outstanding questions for modelling exit strategies that are related to key epidemiological quantities, such as the reproduction number and herd immunity fraction. We then identify different sources of heterogeneity underlying SARS-CoV-2 transmission and control, and consider how differences between hosts and populations across the world should be included in models. Finally, we discuss current challenges relating to data requirements, focusing on the data that are needed to resolve current knowledge gaps and how uncertainty in modelling outputs can be communicated to policymakers and the wider public. In each case, we outline the most relevant issues, summarize expert knowledge and propose specific steps towards the development of evidence-based exit strategies. This leads to a roadmap for future research (figure 1) made up of three key steps: (i) improve estimation of epidemiological parameters using outbreak data from different countries; (ii) understand heterogeneities within and between populations that affect virus transmission and interventions; a
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