Generalization of pairwise models to non-Markovian epidemics on networks

In this Letter, a generalization of pairwise models to non-Markovian epidemics on networks is presented. For the case of infectious periods of fixed length, the resulting pairwise model is a system of delay differential equations, which shows excellent agreement with results based on stochastic simulations. Furthermore, we analytically compute a new R0-like threshold quantity and an analytical relation between this and the final epidemic size. Additionally, we show that the pairwise model and the analytic results can be generalized to an arbitrary distribution of the infectious times, using integro-differential equations, and this leads to a general expression for the final epidemic size. By showing the rigorous link between non-Markovian dynamics and pairwise delay differential equations, we provide the framework for a more systematic understanding of non-Markovian dynamics

Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

Near-real time estimations of the effective reproduction number are among the most important tools to track the progression of a pandemic and to inform policy makers and the general public. However, these estimations rely on reported case numbers, commonly recorded with significant biases. The epidemic outcome is strongly influenced by the dynamics of social contacts, which are neglected in conventional surveillance systems as their real-time observation is challenging. Here, we propose a concept using online and offline behavioral data, recording age-stratified contact matrices at a daily rate. Modeling the epidemic using the reconstructed matrices we dynamically estimate the effective reproduction number during the two first waves of the COVID-19 pandemic in Hungary. Our results demonstrate how behavioral data can be used to build alternative monitoring systems complementing the established public health surveillance. They can identify and provide better signals during periods when official estimates appear unreliable due to observational biases

Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

Monitoring the effective reproduction number R_t R t of a rapidly unfolding pandemic in real-time is key to successful mitigation and prevention strategies. However, existing methods based on case numbers, hospital admissions or fatalities suffer from multiple measurement biases and temporal lags due to high test positivity rates or delays in symptom development or administrative reporting. Alternative methods such as web search and social media tracking are less directly indicating epidemic prevalence over time. We instead record age-stratified anonymous contact matrices at a daily resolution using a longitudinal online-offline survey in Hungary during the first two waves of the COVID-19 pandemic. This approach is innovative, cheap, and provides information in near real-time for estimating R_t R t at a daily resolution. Moreover, it allows to complement traditional surveillance systems by signaling periods when official monitoring infrastructures are unreliable due to observational biases

Risk assessment of novel coronavirus COVID-19 outbreaks outside China

We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the cumulative number of cases in mainland China outside the closed areas; (ii) the connectivity of the destination country with China, including baseline travel frequencies, the effect of travel restrictions, and the efficacy of entry screening at destination; and (iii) the efficacy of control measures in the destination country (expressed by the local reproduction number Rloc ). We found that in countries with low connectivity to China but with relatively high Rloc , the most beneficial control measure to reduce the risk of outbreaks is a further reduction in their importation number either by entry screening or travel restrictions. Countries with high connectivity but low Rloc benefit the most from policies that further reduce Rloc . Countries in the middle should consider a combination of such policies. Risk assessments were illustrated for selected groups of countries from America, Asia, and Europe. We investigated how their risks depend on those parameters, and how the risk is increasing in time as the number of cases in China is growing

Role of A2A adenosine receptors in regulation of opsonized E. coli-induced macrophage function

Adenosine is a biologically active molecule that is formed at sites of metabolic stress associated with trauma and inflammation, and its systemic level reaches high concentrations in sepsis. We have recently shown that inactivation of A2A adenosine receptors decreases bacterial burden as well as IL-10, IL-6, and MIP-2 production in mice that were made septic by cecal ligation and puncture (CLP). Macrophages are important in both elimination of pathogens and cytokine production in sepsis. Therefore, in the present study, we questioned whether macrophages are responsible for the decreased bacterial load and cytokine production in A2A receptor-inactivated septic mice. We showed that A2A KO and WT peritoneal macrophages obtained from septic animals were equally effective in phagocytosing opsonized E. coli. IL-10 production induced by opsonized E. coli was decreased in macrophages obtained from septic A2A KO mice as compared to WT counterparts. In contrast, the release of IL-6 and MIP-2 induced by opsonized E. coli was higher in septic A2A KO macrophages than WT macrophages. These results suggest that peritoneal macrophages are not responsible for the decreased bacterial load and diminished MIP-2 and IL-6 production that are observed in septic A2A KO mice. In contrast, peritoneal macrophages may contribute to the suppressive effect of A2A receptor inactivation on IL-10 production during sepsis