key: cord-0761569-5j4nt2qs
authors: Foddai, Alessandro; Lindberg, Ann; Lubroth, Juan; Ellis-Iversen, Johanne
title: Surveillance to improve evidence for community control decisions during the COVID-19 pandemic – Opening the animal epidemic toolbox for public health
date: 2020-03-27
journal: One Health
DOI: 10.1016/j.onehlt.2020.100130
sha: 5c9e2666e2ef11cb5a92b694127c4f4e69b1b994
doc_id: 761569
cord_uid: 5j4nt2qs

During the first few months of 2020, the COVID-19 pandemic has reached Europe. Health systems all over the world are trying to control the outbreak in the shortest possible time. Exotic disease outbreaks are not uncommon in animal health and randomised surveillance is frequently used as support for decision-making. This editorial discusses the possibilities of practicing One Health, by using methods from animal health to enhance surveillance for COVID-19 to provide an evidence base fort decision-making in communities and countries.

The new coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in late 2019, causing a pandemic. Within a short time, it has spread to countries on all continents disrupting the daily life of people and having serious impact on the world economy. The latest rapid risk assessment from the European Centre for Disease Prevention and Control (ECDC) stated that Europe was heading for sustained community transmission, containment is no longer possible and that community control measures are needed [1] . The virus is likely to spread to the rest of the world within a short time.

Countries are implementing different community control measures. The control measures have profound and long lasting negative effects on society and economy, but when effective, they ensure health systems can keep up with the number of seriously ill people and ultimately s ave lives [2] . The heterogeneity in control approaches is naturally caused by differences in culture, health systems and stage in the epidemic, but may also be influenced by the lack of objective and robust surveillance that maps the evolution of the epidemic and provide evidence to inform control approaches in advance. Approaches to obtain population-based evidence are rapidly needed.

The size and the consequences of the outbreak are unknown. Most countries follow a traditional and vigilant approach of disease surveillance mainly based on case identification (syndromic surveillance), tracing and testing of contacts and high-risk individuals (risk-based surveillance) with daily reporting of: infected, recovered and deaths cases, at area and country level [3] . This allows treatment and isolation of ill people and quarantine of individuals. However, it does not allow warning or forecasting of cases early and ahead of time, nor give an objective overview of the situation to inform decision-making in regards to direction of medical resources or the best timing for community control measures.

J o u r n a l P r e -p r o o f Journal Pre-proof Syndromic surveillance and risk-based surveillance are paramount in epidemics, especially when the disease is emerging and new in a population [4, 5] . The risk-based surveillance on high-risk individuals e.g. contacts, enhances the ability to detect the expected few new cases as soon as possible by targeting those that are more likely to be infected than others. In a very low prevalence or early epidemic scenario, risk-based surveillance is cost-efficient and more likely to find cases than random survey-based surveillance, because resources are targeted at the high-risk subpopulations. However, once the infection becomes established and individual clusters are no longer traced, the usefulness of syndromic or risk-based surveillance data to guide control decisions at community level is reduced, because the cases identified are not representative of the infected individuals in the population. Furthermore, identified cases will already be needing health care and the health systems become reactive to the current situation, rather than proactive in prioritising, what is needed where in the coming weeks.

As veterinary epidemiologists, we applaud the work that is currently being done by public health systems all around the world. It is an impressive effort and it will save many lives. In veterinary The benefits of repeated surveys in control of epidemics are documented in animal health and we encourage public health entities to consider, how these could be adapted to a public health setting during the COVID-19 outbreak. For repeated surveys to be useful for policy-making, they require strong public health leadership, but may benefit from collaboration with veterinary epidemiologists, to build on their experience epidemics in animals. Suggestions and methodology for design of random repeated surveys during the COVID-19 epidemic and for interpreting and translating the outcomes into control decision-support can be found in the accompanying short communication [6] .

J o u r n a l P r e -p r o o f Journal Pre-proof Both COVID-19 and the control measures have profound and long-lasting effects on the world economy, with companies closing, unemployment rocketing, social insecurity rising and increase in deaths as health systems struggle to cope. We strongly encourage the use of robust science for decision-making to ensure evidence-based decisions and to minimise the impacts of the epidemic, and suggest that randomized surveys that generate representative community estimates could provide additional support for policy decisions, in addition to the current surveillance strategies.

Updated rapid risk assessment from ECDC on the novel coronavirus disease 2019 (COVID-19) pandemic: increased transmission in the EU/EEA and the UK

How will country -based mitigation measures influence the course of the COVID-19 epidemic? Lancet, 1-4

Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a

Cases From the Chinese Center for Disease Control and Prevention

Proposed terms and concepts for describing and evaluating animal-health surveillance systems

Base protocol for real time active random surveillance of coronavirus disease (COVID-19) -adapting veterinary methodology to public health. One Health

Funding statement: No funding was received for this work