key: cord-0733634-f1bbq0tj authors: Faustino, Ricardo; Faria, Miguel; Teixeira, Mónica; Palavra, Filipe; Sargento, Paulo; do Céu Costa, Maria title: Systematic review and meta-analysis of the prevalence of coronavirus: One health approach for a global strategy date: 2022-04-04 journal: One Health DOI: 10.1016/j.onehlt.2022.100383 sha: b6172b56e06dda90509624e8c4fe5a284e65bd0c doc_id: 733634 cord_uid: f1bbq0tj Coronaviruses have been responsible for major epidemic crises in 2003 with SARS-CoV-1, in 2012 with MERS-CoV and in 2019 with SARS-CoV-2 (COVID-19), causing serious atypical pneumonia in humans. We intend, with this systematic analysis and meta-analysis, to clarify the prevalence of the various strains of coronavirus in different animal species. For this purpose, we carried out an electronic survey using Pubmed's Veterinary Science search tool to conduct a systematic assessment of published studies reporting the prevalence of different strains of coronavirus in different animal species between 2015 and 2020. We conducted different analysis to assess sensitivity, publication bias, and heterogeneity, using random effect. The final meta-analysis included 42 studies for systematic review and 29 in the meta-analysis. For the geographic regions with a prevalence greater than or equal to 0.20 (Forest plot overall; prevalence = 0.20, p < 0.01, Q = 10,476.22 and I2 = 100%), the most commonly detected viruses were: enteric coronavirus (ECoV), pigeon-dominant coronavirus, (PdCoV), Avian coronavirus M41, Avian coronavirus C46, Avian coronavirus A99, Avian coronavirus JMK, MERS-CoV, Bovine coronavirus, Ro-BatCoV GCCDC1, Alphacoronavirus, Betacoronavirus, Deltacoronavirus, Gamacoronavirus and human coronaviruses (HCoVs). The wide presence of different strains of coronavirus in different animal species on all continents demonstrates the great biodiversity and ubiquity of these viruses. The most recent epidemiological crises caused by coronavirus demonstrates our unpreparedness to anticipate and mitigate emerging risks, as well as the need to implement new epidemiological surveillance programs for viruses. Combined with the need to create advanced training courses in One Health, this is paramount in order to ensure greater effectiveness in fighting the next pandemics. between humans, animals and the environment, and recognizes that the health and wellbeing of human beings is strongly related to the health of animals and their environment [3] . The risk of SARS-CoV-2 transmission to pets and other wild and domestic animals and SARS-CoV-2 potential for unknown animal reservoir hosts and public health implications strongly mandates a one-health strategy to control the COVID-19 and prevent future pandemics [42] . Belonging to the Coronaviridae family, in the order Nidovirales, the SARS-CoV-2 exhibits a genome of positive-sense, single-stranded, polyadenylated, nonsegmented RNA [8] . In order to anticipate the importance and evolution of the coronavirus, a broader point of view is needed to understand the behaviour of Coronaviridae. To date, from the seven coronaviruses reported in humans, four of them are ubiquitous with seasonal circulation and mostly causing relatively mild colds (HKU1, NL63, OC43 and 229E). The other three of more recent zoonotic origin, are associated with severe acute respiratory syndromes, namely SARS-CoV, MERS-CoV and now SARS-CoV-2. Of these seven human coronaviruses, NL63 and 229E belong to the alpha-CoV genus, while the other five are included within the beta-CoV genus. Coronaviruses detected in dogs and cats also belong to these two viral genera [9, 10] . Like SARS-CoV-2 and the other respiratory syndrome viruses, the canine respiratory coronavirus (CRCoV), responsible for a respiratory condition in dogs, belongs to the beta-CoV genus. Canine coronavirus (CCoV) and Feline coronavirus (FCoV), both responsible for digestive diseases, belong to Alphacoronavirus Therefore, the study of the prevalence of different strains of coronaviruses in different animal species in the world provides important information for the implementation of surveillance strategies, as well as epidemiological and preventive public health policies [11, 12] . A systematic assessment of published studies reporting coronaviruses prevalence among different animal species and human, was performed based on PRISMA recommendation [13] . For this purpose, we used the Veterinary Science search tool at PubMed to retrieves published studies, combining different subject search terms, with filtered temporal delimitation in years, between 2015 and 2020. The search strategy used was: veterinary The initial selection by title and abstract was conducted independently by two researchers according to the inclusion/exclusion criteria. In the selection of the titles, we included all those that presented one or more terms with a coronavirus and one health relationship. In a second phase, an exhaustive reading of the articles was carried out to confirm the presence of relevant data to include them in the systematic review and prevalence values for the meta-analysis. Discrepancies in the final decision to include or not an article were discussed with a third investigator to reach a consensus. The PRISMA model was used to organize the information resulting from the article selection process. Only articles dealing with issues related to the coronavirus, both in the scope of human medicine and veterinary medicine, were considered eligible for a One Health approach. The main exclusion criteria were observed: articles published before 2015; studies J o u r n a l P r e -p r o o f unrelated to coronavirus; meta-analyses with data from sources that were not previously published articles with peer review and articles whose full text was not found. Studies without virus prevalence values. Studies related to drug development or optimization of new laboratory techniques were also excluded. Due to the urgency of the situation and the state of the pandemic, studies with indications of public health policies were maintained, as well as other scientific data considered relevant to the discussion. Quantitative and qualitative data extraction from the included studies was performed into four word table and an Excel spreadsheet, containing the following information: author name, year of publication, PubMed article link, article title, animal species, materials and methods, study location, and important note. During the data extraction process, information was extracted by one author and validated by a second author. Disagreements were resolved by discussion and consultation with a third author, whenever necessary. In the evaluation of quality, an instrument adapted from the 22 criteria proposed by the STROBE Statement was used, in compliance with the principles of epidemiological investigation. This assessment aimed to classify the relevance of the articles. The One Health/ERISA evaluation scale, consisting of 15 items to evaluate the articles with regards to the existence of relevant information for the definition of novel One Health recommendations and policies. J o u r n a l P r e -p r o o f For the statistical analysis, data were stored in a predefined spread sheet file, including the authors and year of publication, number of animals and the number of infected animals. Data were analysed using MetaXL version 5.3 software, an add-in for meta-analysis in Microsoft Excel for Windows (https://www.epigear.com/index_files/metaxl.html). The calculated results were represented in table and graphical formats. The heterogeneity across studies was evaluated by Cochrane's Q test and I2 statistics. The calculated value of I2 allows measuring the percentage of variability due to heterogeneity, rather than chance difference or sampling error. If the value of I2 was greater than 50% and the Q test yields P<0.10, heterogeneity was considered statistically significant. The random effects model, based on DerSimonian-Laird method, which calculates the variability within and between studies, was applied to estimate the pooled prevalence and 95% CIs. The transformed double arcsine method was used for situations where the confidence limits and variance instability could appear due to any single studies with larger or small prevalence rates. The Luis Furuya-Kanamori asymmetry index (LFK index) and the Doi plot were calculated to estimate the publication bias. The presence of symmetry indicates no publication bias. The publication bias was determinate by LFK index, which can take the following assessments depending on the value obtained: no asymmetry if the LFK index is within ±1, minor asymmetry when out of the ±1 interval, but within ±2, and major asymmetry if the LFK index is beyond the ± 2 interval. The LFK index for the general metanalysis is 3.71 (major asymmetry), for the mammals class it is 4.23 (major asymmetry), and for the birds class it is 0.34 (no asymmetry). A sensitivity test was calculated to provide an indication of which study is the prime determinant of the pooled result, and which is the main source of heterogeneity. The test J o u r n a l P r e -p r o o f rejects each study, one by one, in the analysis performed, so that it is possible to indicate the combined effect sizes as well as the associated heterogeneity. A total of 115 studies based on the title and abstract were excluded, the remaining 75 studies were selected for the continuation of our study, and 4 studies were subsequently excluded after full reading (2 studies on bibliometrics, 1 study with lack of data and 1 study did not fit the objectives of our study). Of the 71 studies considered eligible, 42 studies were considered for systematic review and 29 studies were included in the metaanalysis ( Figure 1 ). From the one health evaluation scale, we obtained an average score of 9.33 points (62%), the scores of the articles evaluated varied between a minimum of 6 points (40%) and a maximum of 12 points (80%) in a total of 15 points (100%) possible. The following distribution of studies in relation to the average score values, obtained for each of them were: 2 studies had a score of 12 points (80%), 8 studies had a score of 11 points (73.33%), 11 studies had a score of 10 points (66.67%), 8 studies had a score of 9 points (60%), 4 studies had a score of 8 points (50.33%), 7 studies had a score of 7 points (46,67%) and one study had a score of 6 points (40%). Results are shown in Table 1 . Egypt), one in Sweden, one in Australia and 6 global studies ("world") (see Table 1 ). Of the 42 studies selected for the systematic review, thirty-eight (35) contained information on epidemiological data (one study without epidemiological data), twentyfive studies (25) had relevant data for the implementation of One Health policies. The infected animal species described in the studies on the prevalence of different strains of coronavirus in the world were as follows: horses, donkeys, bats (various species), dogs, cats, human, bat, pigeons, camels (more than one species) calves, gull, Magellanic penguins, Dutch pigs, wild boar, alpacas, Llamas, dromedary camel, turkey, quail, chicken, Scandinavian waterfowl and feral camels. The respective identified strains of coronavirus can be found in Table 1 and 2. The prevalence values of the different strains of coronavirus identified in the infected animals and described in the studies selected for the meta-analysis were projected on a world map, where the red colour indicates the maximum prevalence value and the pink signifies the lowest value, see the figure 2. To calculate the meta-analysis of the general group, the random effect was calculated, and the summary measure (overall) of the meta-analysis, have a prevalence value of 0.20 (0.13-0.28, 95% CI), (see figure 3 ). The studies considered were assessed for heterogeneity through the Cochran Q test, calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies, with the weights being those used in the pooling method, and the results (Q = 10476.22, p<0.001) showed that there are variation in study outcomes between studies and variations are due to heterogeneity and not caused by chance. This was confirmed by the value of the I2 test (99.5%), suggesting a statistically significant heterogeneity. Unlike Q, I2 test does not inherently depend upon the number of studies considered. The value of  2 (0.473), which measures the estimated variation (heterogeneity) between the effects observed in different studies also supports the fact that effect sizes vary across studies. A sensitivity analysis of the fifty-one studies was performed to evaluate the effect of each individual study on the pooled result. The results showed that the studies of Falzarano and Müller [14, 15] , were the prime determinants of the pooled result. J o u r n a l P r e -p r o o f Taking into account the great diversity of species found in this study, two groups were organized according to the taxonomic class: the Aves class and mammals. For both cases, the random effect and heterogeneity were calculated. The species that constitute this subgroup are: Pigeon, Great Black-Backed, American Herring Gulls, Magellanic Penguins, Turkey, Quail, Chicken and Scandivian waterfowl. The overall was calculated and the prevalence value is 0.18, with LCI95% of 0.10 and HCI95% of 0.28 (see figure 5 ). We can see that all effects can be considered significant, since none of the 95% confidence intervals shows a lower limit for the prevalence values below zero, therefore none of these intervals crosses the dividing line. The results obtained in this systematic review and meta-analysis demonstrate a wide Tables 1 and 4) . If we consider the geographic regions with a prevalence greater than or equal to 0.20 CoV-2 inconclusive, although new evidence points to the bat [11] . The meta-analysis of the subgroups shows a higher prevalence in the Aves class 0.24 to animals in the food production chain. Therefore, the food becomes an important vehicle for many, but not all, of these zoonotic pathogens, lacking plans for systematic collaboration between authorities and stakeholders in the animal health, food control and human health sectors [43] . .A pioneering review in 2018, discusses food safety aspects of importance from a One Health perspective, focusing on Europe [44] . Using examples of food pathogen/food commodity combinations, spread of antimicrobial resistance in the food web and the risk of transmission of zoonotic pathogens in a circular system, it demonstrates how different perspectives are interconnected. Regarding food, the first incident related to COVID-19 occurred on June 12, 2020 at the Xinfadi agricultural products market in Beijing, where SARS-CoV-2 was detected on a cutting board used to process imported salmon [27] . Although subsequent investigations have not been conclusive as to its origin, this particular incident raised, before authorities and consumers, some questions about frozen foods as possible carriers of SARS-CoV-2. Since the beginning of July 2020, at least nine food contamination incidents have been reported in China, where SARS-CoV-2 has been detected in imported foods, mainly packaging materials, from shrimp imported from Ecuador, and in Shenzhen, in Guangdong province on August 12, 2020, on the surface of frozen chicken originating in Brazil, which became the first known case in which the new coronavirus was detected in real samples of imported foods [27] . It is worth considering the possibility that the food cold chain may promote contamination, because laboratory studies [28] have shown that SARS-CoV-2 remained highly stable under refrigeration, at 4° C, and in freezing conditions, from -10 to -80 °C J o u r n a l P r e -p r o o f in fish, meat, poultry, and pig skin for 14-21 days. In a controlled laboratory study [29] , the persistence of SARS-CoV-2 in chilled salmon, frozen chicken and pork for 21 days was examined. The study showed that SARS-CoV-2 titers remained virtually constant, and the inoculated viruses maintained their infectivity both in the refrigerated product (4 °C) and in the frozen samples (-20 °C and -80 °C). In a previous study, researchers presented evidence that SARS-CoV-2 can remain quite stable in pig skin for 14 days, at 4 °C [30] . The most recent investigation, in an experimental context, points to the new coronavirus remaining up to 72 hours in plastic and stainless steel with temperatures of around 20º and humidity of 40% [31, 32] . Other multiple investigations have already reported that SARS-CoV-2 and other coronaviruses are able to remain on surfaces such as metal, glass, PVC, Teflon, and other materials, for several days [33, 34, 35, 36] . At the interface between the health of humans, animals and the ecosystem, host receptor recognition is a determinant for virus infection. Li [37] conducted sequence and structural analyses of angiotensin-converting enzyme 2 (ACE2) from different species, which sheds some light on cross-species receptor usage of SARS-CoV-2. Citing the authors, all these analyses raise an alert on a potential interspecies transmission of the virus and propose further surveillance in the diverse animal populations. This systematic review and meta-analysis of the prevalence of the coronaviruses worldwide highlights the great biological diversity of these agents, as well as their only showed our lack of preparedness and responsiveness at the global level. Gathering data for collaborative, multisectoral, and trans-disciplinary approachworking at the local, regional, national, and global levels is the only way to be better prepared for the next One Health threat. Ricardo Faustino: conceptualization, data management, metanalysis, investigation, methodology, validation, writing -original draft, review, editing, supervision. Paulo Sargento: investigation, methodology, validation, review and editing. Maria do Céu Costa: conceptualization, investigation, methodology, validation, Writing and Review Mónica Teixeira: conceptualization, investigation, methodology, validation, writing and review. Miguel Faria: Investigation, methodology, validation, review metanalysis, writing and editing. 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