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To control an epidemic, authorities will often impose varying degrees of lockdown. In a paper in the journal Chaos, scientists have discovered, using mathematics and computer simulations, why dividing a large population into multiple subpopulations that do not intermix can help contain outbreaks without imposing contact restrictions within those local communities.

Numerical results show that school closure alone would have limited benefit in reducing the peak incidence (less than 10% reduction with 8-week school closure for regions in the early phase of the epidemic). When coupled with 25% adults teleworking, 8-week school closure would be enough to delay the peak by almost 2 months with an approximately 40% reduction of the case incidence at the peak. This is critical to reduce the burden on the healthcare system in the weeks of highest demand. Moderate overall reduction of the final attack rate (15%) would also be achieved. Results across regions are qualitatively similar, with differences

People the world over have acquired a passing familiarity with epidemic models, critical threshold values for transmission (R0), comorbidities, and exponential growth. But these insights into the pandemic are like Harrison's H1-3; they focus on a few elements of the problem. The pandemic is every bit as much a problem of the non-linear dynamics of markets, the cognitive biases of decision-makers, the collective dynamics of groups, and the coevolution of biological species - humans, mammalian food sources, and viral agents.

Research from the University of Notre Dame estimates that more than 100,000 people were already infected with COVID-19 by early March-when only 1,514 cases and 39 deaths had been officially reported and before a national emergency was declared. The study provides insight into how limited testing and gaps in surveillance during the initial phase of the epidemic resulted in so many cases going undetected. The study was published in the Proceedings of the National Academy of Sciences.

Case isolation and contact tracing can contribute to the control of COVID-19 outbreaks1,2. However, it remains unclear how real-world social networks could influence the effectiveness and efficiency of such approaches. To address this issue, we simulated control strategies for SARS-CoV-2 transmission in a real-world social network generated from high-resolution GPS data that were gathered in the course of a citizen-science experiment3,4. We found that tracing the contacts of contacts reduced the size of simulated outbreaks more than tracing of only contacts, but this strategy also resulted in almost half of the local population being quarantined at a single point in time. Testing and releasing non-infectious individuals from quarantine led to increases in outbreak size, suggesting that contact tracing and quarantine might be most effective as a 'local lockdown' strategy when contact rates are high. Finally, we estimated that combining physical distancing with contact tracing could enable epidemic control while reducing the number of quarantined individuals. Our findings suggest that targeted tracing and quarantine strategies would be most efficient when combined with other control measures such as physical distancing.