CDC (Cindy and David's Conversations) Game: Advising President to Survive Pandemic.

Ongoing debates on anti-COVID19 policies have been focused on coexistence-with vs. zero-out (virus) strategies, which can be simplified as "always open (AO)" vs. "always closed (AC)." We postulate that a middle ground, dubbed LOHC (low-risk-open and high-risk-closed), is likely favorable, precluding obviously irrational HOLC (high-risk-open and low-risk-closed). From a meta-strategy perspective, these four policies cover the full spectrum of anti-pandemic policies. By emulating the reality of anti-pandemic policies today, the study aims to identify possible cognitive gaps and traps by harnessing the power of evolutionary game-theoretic analysis and simulations, which suggest that (i) AO and AC seems to be "high-probability" events (0.412-0.533); (ii) counter-intuitively, the middle ground-LOHC-seems to be small-probability event (0.053), possibly mirroring its wide adoptions but broad failures. Besides devising specific policies, an equally important challenge seems to deal with often hardly avoidable policy transitions along the process from emergence, epidemic, through pandemic, to endemic state.

The Upper Respiratory Tract Microbiome Network Impacted by SARS-CoV-2.

The microbiome of upper respiratory tract (URT) acts as a gatekeeper to respiratory health of the host. However, little is still known about the impacts of SARS-CoV-2 infection on the microbial species composition and co-occurrence correlations of the URT microbiome, especially the relationships between SARS-CoV-2 and other microbes. Here, we characterized the URT microbiome based on RNA metagenomic-sequencing datasets from 1737 nasopharyngeal samples collected from COVID-19 patients. The URT-microbiome network consisting of bacteria, archaea, and RNA viruses was built and analyzed from aspects of core/periphery species, cluster composition, and balance between positive and negative interactions. It is discovered that the URT microbiome in the COVID-19 patients is enriched with Enterobacteriaceae, a gut associated family containing many pathogens. These pathogens formed a dense cooperative guild that seemed to suppress beneficial microbes collectively. Besides bacteria and archaea, 72 eukaryotic RNA viruses were identified in the URT microbiome of COVID-19 patients. Only five of these viruses were present in more than 10% of all samples, including SARS-CoV-2 and a bat coronavirus (i.e., BatCoV BM48-31) not detected in humans by routine means. SARS-CoV-2 was inhibited by a cooperative alliance of 89 species, but seems to cooperate with BatCoV BM48-31 given their statistically significant, positive correlations. The presence of cooperative bat-coronavirus partner of SARS-CoV-2 (BatCoV BM48-31), which was previously discovered in bat but not in humans to the best of our knowledge, is puzzling and deserves further investigation given their obvious implications. Possible microbial translocation mechanism from gut to URT also deserves future studies.

Predicting the Outbreak Risks and Inflection Points of COVID-19 Pandemic with Classic Ecological Theories.

Predicting the outbreak risks and/or the inflection (turning or tipping) points of COVID-19 can be rather challenging. Here, it is addressed by modeling and simulation approaches guided by classic ecological theories and by treating the COVID-19 pandemic as a metapopulation dynamics problem. Three classic ecological theories are harnessed, including TPL (Taylor's power-law) and Ma's population aggregation critical density (PACD) for spatiotemporal aggregation/stability scaling, approximating virus metapopulation dynamics with Hubbell's neutral theory, and Ma's diversity-time relationship adapted for the infection-time relationship. Fisher-Information for detecting critical transitions and tipping points are also attempted. It is discovered that: (i) TPL aggregation/stability scaling parameter (b > 2), being significantly higher than the b-values of most macrobial and microbial species including SARS, may interpret the chaotic pandemic of COVID-19. (ii) The infection aggregation critical threshold (M 0) adapted from PACD varies with time (outbreak-stage), space (region) and public-health interventions. Exceeding M 0, local contagions may become aggregated and connected regionally, leading to epidemic/pandemic. (iii) The ratio of fundamental dispersal to contagion numbers can gauge the relative importance between local contagions vs. regional migrations in spreading infections. (iv) The inflection (turning) points, pair of maximal infection number and corresponding time, are successfully predicted in more than 80% of Chinese provinces and 68 countries worldwide, with a precision >80% generally.