  Controlling the COVID-19 pandemic is an urgent global challenge. The rapid
geographic spread of SARS-CoV-2 directly reflects the social structure. Before
effective vaccines and treatments are widely available, we have to rely on
alternative, non-pharmaceutical interventions, including frequency testing,
contact tracing, social distancing, mask wearing, and hand-washing, as public
health practises to slow down the spread of the disease. However frequent
testing is the key in the absence of any alternative. We propose a network
approach to determine the optimal low resources setting oriented pool testing
strategies that identifies infected individuals in a small number of tests and
few rounds of testing, at low prevalence of the virus. We simulate stochastic
infection curves on societies under quarantine. Allowing some social
interaction is possible to keep the COVID-19 curve flat. However, similar
results can be strategically obtained searching and isolating infected persons
to preserve a healthier social structure. Here, we analyze which are the best
strategies to contain the virus applying an algorithm that combine samples and
testing them in groups [1]. A relevant parameter to keep infection curves flat
using this algorithm is the dairy frequency of testing at zones where a high
infection rate is reported. On the other hand, the algorithm efficiency is low
for random search of infected people.
