Intervention strategies with 2D cellular automata for testing SARS-CoV-2 and reopening the economy.

During the period of time between a new disease outbreaks and its vaccine is deployed, the health and the economic systems have to find a testing strategy for reopening activities. In particular, asymptomatic individuals, who transmit locally the COVID-19 indoors, have to be identified and isolated. We proposed a 2D cellular automaton based on the SI epidemic model for selecting the most desirable testing frequency and identifying the best fitting size of random trails on local urban environments to diagnose SARS-CoV-2 and isolate infected people. We used the complex systems approach to face the challenge of a large-scale test strategy based on urban interventions, starting with first responders and essential workers. We used the case of Mexico to exemplify a credible and intelligent intervention that reduces the virus transmission and detects economic and health costs. Findings suggest that controlling and stopping the virus transmission in a short period of time are possible if the frequency of testing is daily and the percentage of random samples to be tested is at least 90%. This combination of model parameters represents the least expensive intervention compared to others. Therefore, the key for a national testing-isolating strategy is local interventions.

Horseback riding pathways and harbors at the beginning of the colonial era in Mexico.

The introduction of horses in the New World changed the way of traveling on complex terrains. This change reconfigured the land transport network connecting harbors in the region. However, data of horseback riding pathways among harbors is scarce. We analyzed the case of Mexico at the beginning of the colonial period to recreate routes that connected ancient harbors and to identify the network characteristics of a large-scale system of routes. We used the complex systems approach as a framework in which we applied the least cost path analysis to reconstruct a network of horseback paths, and we computed the node betweenness centrality to identify the most probable locations that controlled de flow of travels. Findings suggest that horses modified the transportation system by expanding the connections and increasing the speed of traveling across the New Spain territory. The node betweenness centrality suggests that some locations organized the flow of traveling based on a few harbors located at the central region. Therefore, the horse allowed the Spaniards to reshape the spatial organization in the colonial era in Mexico.

Nonlinearity and distance of ancient routes in the Aztec Empire.

This study explores the way in which traveling paths in ancient cultures are characterized by the relationship between nonlinear shapes and path lengths in terms of distances. In particular, we analyze the case of trade routes that connected Aztec settlements around 1521 CE in central Mexico. Based on the complex systems perspective, we used the least cost path approximation to reconstruct a hypothetical large-scale map of routes reproducing physical connections among ancient places. We compared these connections with different spatial configurations and identified the probability distribution functions of path lengths. We evaluated the nonlinearity using the mean absolute error based on the path fitness of simple linear models. We found asymmetrical distributions and positive relationships between those measures. If a path length increases, so does its nonlinearity. Thus, the simple pattern of traveling in the Aztec region is fairly unlikely to be straight and short. Complex pathways can represent most of the ancient routes in central Mexico.

Determination of the complexity of distance weights in Mexican city systems.

This study tests distance weights based on the economic geography assumption of straight lines and the complex networks approach of empirical road segments in the Mexican system of cities to determine the best distance specification. We generated network graphs by using geospatial data and computed weights by measuring shortest paths, thereby characterizing their probability distributions and comparing them with spatial null models. Findings show that distributions are sufficiently different and are associated with asymmetrical beta distributions. Straight lines over- and underestimated distances compared to the empirical data, and they showed compatibility with random models. Therefore, accurate distance weights depend on the type of the network specification.