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For any emerging pathogen, the preferred approach is to drive it to extinction with non-pharmaceutical interventions (NPI) or suppress its spread until effective drugs or vaccines are available. However, this might not always be possible. If containment is infeasible, the best people can hope for is pathogen transmission until population level immunity is achieved, with as little morbidity and mortality as possible.
A simple computational model was used to explore how people should choose NPI in a non-containment scenario to minimize mortality if mortality risk differs by age.
Results show that strong NPI might be worse overall if they cannot be sustained compared to weaker NPI of the same duration. It was also shown that targeting NPI at different age groups can lead to similar reductions in the total number of infected, but can have strong differences regarding the reduction in mortality.
Strong NPI that can be sustained until drugs or vaccines become available are always preferred for preventing infection and mortality. However, if people encounter a worst-case scenario where interventions cannot be sustained, allowing some infections to occur in lower-risk groups might lead to an overall greater reduction in mortality than trying to protect everyone equally.
SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9$ \% $ to 62.8$ \% $$ (p<0.001) $; the average shortest path length decreased from 1.53 to 1.14 $ (p<0.001) $; the average betweenness reduced from 0.65 to 0.11$ (p<0.001) $; the average cluster size dropped from 4.05 to 2.72 $ (p=0.004) $; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 $ (p=0.099). $ Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.
The coronavirus disease 2019 (COVID-19) pandemic prompted universities across the United States to close campuses in Spring 2020. Universities are deliberating whether, when, and how they should resume in-person instruction in Fall 2020. In this essay, we discuss some practical considerations for the use of 2 potentially useful control strategies based on testing: (1) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcriptase-polymerase chain reaction (RT-PCR) testing followed by case-patient isolation and quarantine of close contacts, and (2) serological testing followed by an “immune shield” approach, that is, low social distancing requirements for seropositive persons. The isolation of case-patients and quarantine of close contacts may be especially challenging, and perhaps prohibitively difficult, on many university campuses. The “immune shield” strategy might be hobbled by a low positive predictive value of the tests used in populations with low seroprevalence. Both strategies carry logistical, ethical, and financial implications. The main nonpharmaceutical interventions will remain methods based on social distancing (eg, capping class size) and personal protective behaviors (eg, universal facemask wearing in public space) until vaccines become available, or unless the issues discussed herein can be resolved in such a way that using mass testing as main control strategies becomes viable.
Our aim was to investigate whether there is a season-of-birth effect in
anorexia nervosa. In a meta-analysis, we compared the distribution of
anorexia births (n = 1293) from four independent UK cohorts
to that of the general UK population (n = 21 914 037),
using both the Walter & Elwood seasonality and chi-squared tests. We
found an excess of anorexia births from March to June (odds ratio (OR) =
1.15, 95% CI 1.03–1.29, P = 0.012) and a deficit from
September to October (OR = 0.8, 95% CI 0.68–0.94, P =
0.007). These results indicate that environmental risk factor(s) are
operative during gestation or immediately after birth and their
identification will be important for disease prevention strategies.
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