SARS and other coronaviruses in humans and animals

What Can COVID-19 Teach Us about Using AI in Pandemics?

The COVID-19 pandemic put significant strain on societies and their resources, with the healthcare system and workers being particularly affected. Artificial Intelligence (AI) offers the unique possibility of improving the response to a pandemic as it emerges and evolves. Here, we utilize the WHO framework of a pandemic evolution to analyze the various AI applications. Specifically, we analyzed AI from the perspective of all five domains of the WHO pandemic response. To effectively review the current scattered literature, we organized a sample of relevant literature from various professional and popular resources. The article concludes with a consideration of AI’s weaknesses as key factors affecting AI in future pandemic preparedness and response.

Development of a dose-response model for SARS coronavirus

In order to develop a dose-response model for SARS coronavirus (SARS-CoV), the pooled data sets for infection of transgenic mice susceptible to SARS-CoV and infection of mice with murine hepatitis virus strain 1, which may be a clinically relevant model of SARS, were fit to beta-Poisson and exponential models with the maximum likelihood method. The exponential model (k= 4.1 x l0(2)) could describe the dose-response relationship of the pooled data sets. The beta-Poisson model did not provide a statistically significant improvement in fit. With the exponential model, the infectivity of SARS-CoV was calculated and compared with those of other coronaviruses. The does of SARS-CoV corresponding to 10% and 50% responses (illness) were estimated at 43 and 280 PFU, respectively. Its estimated infectivity was comparable to that of HCoV-229E, known as an agent of human common cold, and also similar to those of some animal coronaviruses belonging to the same genetic group. Moreover, the exponential model was applied to the analysis of the epidemiological data of SARS outbreak that occurred at an apartment complex in Hong Kong in 2003. The estimated dose of SARS-CoV for apartment residents during the outbreak, which was back-calculated from the reported number of cases, ranged from 16 to 160 PFU/person, depending on the floor. The exponential model developed here is the sole dose-response model for SARS-CoV at the present and would enable us to understand the possibility for reemergence of SARS.

Potential for the replication of the betanodavirus redspotted grouper nervous necrosis virus in human cell lines

The determination of the host ranges of viruses is important because of the possible emergence of infectious agents, which may result from the zoonotic transmission of animal viruses to humans. The family Nodaviridae, whose members are non-enveloped, positive-stranded bipartite RNA viruses, is comprised of the genera Alphanodavirus and Betanodavirus, whose members predominantly infect insects and fish, respectively. The alphanodaviruses can also infect suckling mice and suckling hamsters, resulting in paralysis and death. Pigs near the site of isolation of the Nodamura virus (NoV), an alphanodavirus, have been reported to have high levels of NoV neutralizing antibody, suggesting that they may be part of the natural host range of this virus. Betanodaviruses are the causative agents of viral nervous necrosis, which occurs in several species of fish. However, little is known regarding the mechanism of infection of these viruses. Whether betanodaviruses can infect hosts other than fish remains unclear. In this study, we examined the possibility that a betanodavirus, redspotted grouper nervous necrosis virus (RGNNV), can infect human cell lines and showed that this virus can attach to the cells but cannot penetrate them, although human cells can support the replication of the betanodavirus when viral RNAs are transfected. The betanodavirus in its present form cannot infect human cells.