DNA damage contributes to age-associated differences in SARS-CoV-2 infection.

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to disproportionately affect older individuals. How aging processes affect SARS-CoV-2 infection and disease progression remains largely unknown. Here, we found that DNA damage, one of the hallmarks of aging, promoted SARS-CoV-2 infection in vitro and in vivo. SARS-CoV-2 entry was facilitated by DNA damage caused by extrinsic genotoxic stress or telomere dysfunction and hampered by inhibition of the DNA damage response (DDR). Mechanistic analysis revealed that DDR increased expression of angiotensin-converting enzyme 2 (ACE2), the primary receptor of SARS-CoV-2, by activation of transcription factor c-Jun. Importantly, in vivo experiment using a mouse-adapted viral strain also verified the significant roles of DNA damage in viral entry and severity of infection. Expression of ACE2 was elevated in the older human and mice tissues and positively correlated with γH2AX, a DNA damage biomarker, and phosphorylated c-Jun (p-c-Jun). Finally, nicotinamide mononucleotide (NMN) and MDL-800, which promote DNA repair, alleviated SARS-CoV-2 infection and disease severity in vitro and in vivo. Taken together, our data provide insights into the age-associated differences in SARS-CoV-2 infection and a novel approach for antiviral intervention.

Development and utility of a close contact information management system for the COVID-19 pandemic.

BACKGROUND: Since the outbreak started in 2019, COVID-19 pandemic has a significant global impact. Due to the highly infective nature of SARS-CoV-2, the COVID-19 close contacts are at significant risk of contracting COVID-19. China's experience in successfully controlling COVID-19 emphasized the importance of managing close contacts because this strategy helps to limit potential infection sources, prevent the unconscious spread of COVID-19 and thus control this pandemic. As a result, to understand and consider the management of close contacts may be beneficial to other countries. However, managing close contacts is challenging owing to the huge number of close contacts and a lack of appropriate management tools and literature references. METHODS: A new system called the COVID-19 Close Contact Information Management System was developed. Here we introduced the design, use, improvement and achievements of this system. RESULTS: This system was designed from the standpoint of the Centers for Disease Control and Prevention in charge of managing close contacts. Two main functions and eight modules/themes were ultimately formed after two development stages. The system introduces what information need to be collected in the close contact management. Since the system allows information flow across cities, the geographical distance and administrative regional boundaries are no longer obstacles for managing close contacts, which promotes the management of each close contact. Moreover, when this system is used in conjunction with other data tools, it provides data assistance for understanding the COVID-19 characteristics and formulating targeted COVID-19 control policies. To date, the system has been widely used in Guangdong Province for over 1 year and has recorded tens of thousands of pieces of data. There is sufficient practical experience to suggest that the system is capable of meeting the professional work requirements for close contact management. CONCLUSIONS: This system provides a new way to manage close contacts and restrict the spread of COVID-19 by combining information technology with disease prevention and control strategies in the realm of public health. We hope that this system will serve as an example and guide for those anticipating similar work in other countries in response to current and future public health incidents.

Projecting and comparing non-pharmaceutical interventions to contain COVID-19 in major economies (preprint)

Non-pharmaceutical interventions (NPIs) such as quarantine, self-isolation, social distancing, and virus-contact tracing can greatly reduce the spread of the virus during a pandemic. In the wave of the COVID-19 pandemic, many countries have implemented various NPIs for infection control and mitigation. However, the stringency of the NPIs and the resulting impact among different countries remain unclear due to the lack of quantitative factors. In this study we took a further step to incorporate the effect of the NPIs into the pandemic dynamics model using the concept of policy intensity factor (PIF). This idea enables us to characterize the transition rates as time varying quantities instead of constant values, and thus capturing the dynamical behavior of the basic reproduction number variation in the pandemic. By leveraging a great amount of data reported by the governments and the World Health Organization, we projected the dynamics of the pandemic for the major economies in the world, including the numbers of infected, susceptible, and recovered cases, as well as the pandemic durations. It is observed that the proposed variable-rate susceptible-exposed-infected-recovered (VR-SEIR) model fits and projects the pandemic dynamics very well. We further showed that the resulting PIFs correlate with the stringency of NPIs, which allows us to project the final affected numbers of people in those countries when their current NPIs have been imposed for 90, 180, 360 days. It provides a quantitative insight into the effectiveness of the implemented NPIs, and sheds a new light on minimizing both affected people from COVID-19 and the economic impact.

Phase adjusted estimation of the number of 2019 novel coronavirus cases in Wuhan, China (preprint)

An outbreak of clusters of viral pneumonia due to a novel coronavirus (2019-nCoV / SARS-CoV-2) happened in Wuhan, Hubei Province in China in December 2019. Since the outbreak, several groups reported estimated R0 of Coronavirus Disease 2019 (COVID-19) and generated valuable prediction for the early phase of this outbreak. After implementation of strict prevention and control measures in China, new estimation is needed. An infectious disease dynamics SEIR (Susceptible, Exposed, Infectious and Removed) model was applied to estimate the epidemic trend in Wuhan, China under two assumptions of Rt. In the first assumption, Rt was assumed to maintain over 1. The estimated number of infections would continue to increase throughout February without any indication of dropping with Rt = 1.9, 2.6 or 3.1. The number of infections would reach 11,044, 70,258 and 227,989, respectively, by 29 February 2020. In the second assumption, Rt was assumed to gradually decrease at different phases from high level of transmission (Rt = 3.1, 2.6 and 1.9) to below 1 (Rt = 0.9 or 0.5) owing to increasingly implemented public heath intervention. Several phases were divided by the dates when various levels of prevention and control measures were taken in effect in Wuhan. The estimated number of infections would reach the peak in late February, which is 58,077-84,520 or 55,869-81,393. Whether or not the peak of the number of infections would occur in February 2020 may be an important index for evaluating the sufficiency of the current measures taken in China. Regardless of the occurrence of the peak, the currently strict measures in Wuhan should be continuously implemented and necessary strict public health measures should be applied in other locations in China with high number of COVID-19 cases, in order to reduce Rt to an ideal level and control the infection.