Epidemiologically speaking, it has been more than four months since the first reported cases of the novel coronavirus 2019 (2019-nCoV) in Wuhan, China, considered as a new type of human Betacoronavirus, having over 96% identity with severe acute respiratorysyndrome coronavirus (SARS-CoV) according to whole-genome sequence analysis (Malik et al. 2020; Monchatre-Leroy et al. 2017). The COVID-19 outbreak subsequently began spreading around the world, threatening global public health due to the lack of any specific therapeutic agents (Sun et al. 2020). As of December 8, 2020, the global confirmed cases and deaths are 66,729,375 and 1,535,982, according to the World Health Organization (WHO) (i.e., https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports). Recent reports reveal that a clinical diagnosis of COVID-19 is possible using chest X-ray examination, similar to SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV) diseases (Das et al. 2015; Nicolaou et al. 2003). Also, laboratory diagnostic techniques such as real-time polymerase chain reaction (PCR) and quantitative reverse transcription PCR (RT-PCR) have demonstrated excellent performance for the detection of the disease in the early stages (Chu et al. 2020; Corman et al. 2020; Wu et al. 2020). The latest studies have investigated the epidemiological aspects of COVID-19 in terms of incidence and mortality rates by country (Ahn et al. 2020; Lai et al. 2020; Sun et al. 2020; Zhai et al. 2020). Reports have shown that SARS-CoV and MERS-CoV diseases have higher case fatality rates in comparison to SARS-CoV-2 (Park et al. 2020). Systematic reviews and meta-analysis studies are commonly designed for health-related issues based on available research evidence and provide a knowledge base outcome for health policymakers (Gopalakrishnan and Ganeshkumar 2013; Malterud et al. 2018).

The rationale behind the development of monitoring devices and tools specifically for the health care community, whether wearable, portable or online, is to provide sufficient data regarding various vital patient parameters in order to propose critical care and diagnosis, and in wider aspects to study and investigate the epidemiological effects of a disease or pandemic on a specific population. Wearable and portable health devices are used mostly for monitoring patients’ health status such as blood pressure, oxygen rate, and heart rate at home or hospitals. The online monitoring health tools, on the other hand, are commonly developed based on available data to analyze and propose an epidemiological event which will be beneficial for health policymakers to enable appropriate decision-making with regard to actions to stop or mitigate the disease risk. Health-related monitoring tools will be important when periodic investigation of the recorded analyses for a disease result in a disaster situation, and the COVID-19 pandemic is no exception in this regard. In this case, various available evidence has been used for COVID-19, for instance, in terms of its transmission potential, that shows disastrous effects on the world’s population (Tariq et al. 2020; Xu and Kraemer 2020). Generally, an effective health device or tool comprises three main components, including data acquisition, monitoring, and control, that are responsible for collecting data from a source, performing the tracking activities, and making decisions based on the monitoring component (Singh et al. 2020). However, some of the online monitoring tools may ignore the final decision-making process. A recent study also showed that policymakers and health professionals accounted for more than half of the most knowledgeable users that may play a critical role in providing awareness for a population (Al-Riyami 2010; Tricco et al. 2018). Additionally, there may be some gaps between policymakers and health researchers in terms of their points of view in interpreting the synthesized health information, and these gaps will need to be addressed in the future using decision-making systems based on artificial intelligents (Al-Riyami 2010).

In the current pandemic situation, several monitoring tools are engaged to provide meaningful synthesized or raw information. A global map monitoring tool developed at Johns Hopkins University provides information for countries on cumulative cases, active cases, incidence rate, case fatality ratio, and testing rate. The Johns Hopkins interactive web-based dashboard is able to track the COVID-19 pandemic in real time to provide updated information twice a day (Dong et al. 2020). The current Meta-MUMS COVID-19 web server is different from the Johns Hopkins real-time tool. It takes advantage of the data for COVID-19 and SARS-CoV included in worldometers.info and WHO as input. It performs a meta-analysis using whole data and proposes the results for five parameters, including incidence rate, mortality rate, recovery rate, incidence vs. death, and recovery vs. death. The online Meta-MUMS COVID-19 web server is intended to provide the results of thorough comparative meta-analysis between COVID-19 and SARS-CoV and is updated once a day at 00:00 UTC. Finally, the Meta-MUMS COVID-19 will demonstrate the outcome of the subgroup meta-analysis, where health policymakers can easily use it as knowledge-based information for future decisions.

To the best of the authors’ knowledge, no studies have reported the development of a web server for performing an online cumulative meta-analysis to compare two SARS-CoV-2 and SARS-CoV diseases through incidence, mortality, and recovery cases. In the current study, we introduce a web server, namely Meta-MUMS (“Meta” is a shortened form for meta-analysis and “MUMS” stands for Mashhad University of Medical Sciences) COVID-19. It performs daily online comparative meta-analysis between two diseases using the cumulative global COVID-19 and SARS-CoV data extracted from Worldometer (https://www.worldometers.info/coronavirus/) and https://www.who.int/csr/sars/country/country2003_08_15.pdf?ua=1.

The features of the Meta-MUMS COVID-19 web server include the ability to (i) compare incidence rates by event rate, (ii) analyze mortality rates by event rate, (iii) compare recovery rates by event rate, (iv) compare incidence vs. mortality rates using risk ratios, (v) compare recovery vs. mortality rates using risk ratios, (vi) provide knowledge-based descriptions for all five forest plots considering p-values less than 0.05 as significant, and (vii) provide daily updates.

The inputs for performing the meta-analysis include “country names,” “cumulative number of cases,” “cumulative number of deaths,” “cumulative number of recovered cases,” and “population of the countries.” For “N/A” data, the corresponding country was excluded from the analysis. The programming environment for the meta-analysis is the MATLAB environment, in which the web app for this study is deployed. The analyzed measurements between SARS-CoV-2 and SARS-CoV diseases were event rate and risk ratio using a random-effects model. Additionally, for SARS-CoV-2, the countries with incidence cases known as critical by WHO risk assessment set as high for China’s pandemic were selected to be included in the outcome.

Therefore, the Meta-MUMS COVID-19 relies on the data extracted (i.e., “country names,” “cumulative number of cases,” “cumulative number of deaths,” “cumulative number of recovered cases,” and “population of the countries”) from worldometers.info, which is also updated in a real-time manner, as well as the constant SARS-CoV WHO data, and is implemented through a MATLAB web server compiled as a web service app (i.e., ctf file). The development and web server rental charge will be covered throughout the COVID-19 pandemic period; however, all of the expenses will be borne by personal accounts.

The Meta-MUMS COVID-19 web server is available at http://144.76.160.207:9988/webapps/home/session.html?app=Meta_MUMS_COVID19.

(as shown in Fig. 1).

Fig. 1
figure 1

The first page of the MetaMUMS COVID-19 web server. (a) The updated data for the COVID-19 database. (b) Users can select the meta-analysis type from the radio buttons, (c) the forest plot for the selected meta-analysis will be illustrated for the SARS-CoV-2 and SARS-CoV datasets, and (d) the interpretation for each forest plot will be demonstrated

Full size image