Mathematical Recommendations to Fight Against COVID-19

Urban nature as a source of resilience during social distancing amidst the coronavirus pandemic

“…Hence, to avoid spread of virus, authorities world-wide have prescribed social distancing that ranges from isolation among people in entire metropolitan areas and commanding inhabitants to stay home (shelter in place), to the closing down of meeting places and schools, and voluntary isolation of elders and other vulnerable sub-groups. While these measures can be effective against disease transmission (Gu et al, 2020;Tian et al, 2020;Wilder-Smith & Freedman, 2020), they also often result in social isolation for many people. Social isolation can induce feelings of loneliness, with negative well-being consequences , such as increased risks of anxiety (Cacioppo et al, 2006), depression (Cacioppo, Hawkley, & Thisted, 2010) and premature mortality (Holt-Lunstad, Smith, Baker, Harris, & Stephenson, 2015).…”

“…Such measures include school closures and urging people to stay home, and centre around reducing the number of close physical interactions among people. It is widely regarded as one of the most effective approaches to keep COVID-19 cases down (Gu, Jiang, Zhao, & Zheng, 2020;Tian et al, 2020;Wilder-Smith & Freedman, 2020). Social distancing has simultaneously disrupted the everyday lives of entire populations wherever it has been implemented (Caria et al, 2020).…”

Reducing SARS-CoV-2 infectious spreading patterns by removing S and R compartments from SIR model equation

“…In order to extract fixed point orientation with not homologous data involved in the epidemics cases, concerning the system global homomorphism over time, no stability pattern was found for S and R in recent researches [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] except for I [24][25][26][27][28][29][30][31][32][33]. This stability feature for S and R is not commonly observed in the empirical terms of the analysis for short time periods of observation while these data presented time-varying empirical data and unresolved partial differential equations on the basis of epidemics evolution and this still applies for long time periods as well [1,2,7,37].…”

“…This stability feature for S and R is not commonly observed in the empirical terms of the analysis for short time periods of observation while these data presented time-varying empirical data and unresolved partial differential equations on the basis of epidemics evolution and this still applies for long time periods as well [1,2,7,37]. While S and R periodic fixed point that could uncover a stability pattern formation needed for a deterministic SIR modeling are not obtained (vaccines/drugs/immunization), results, nowadays [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] generates probabilistic distributions permeating the system for short time lengths (local homomorphism) and long time lengths (global homomorphism) generating distinct distributions shapes and scales for each country.…”

“…These authors presented descripted analysis of SIR models and variants failures to achieve a good to fit predictability and sensitivity of how SARS-CoV-2 infectious spreading patterns might occurs over time (also this limitation being live verifiable by automated tools data bases available to perform it at https://projects.fivethirtyeight.com/covid-forecasts/ [7] as exemplified in "S1 figure". Many researches based on SIR models or its variants [8], just to mention very few examples [7,[9][10][11][12][13][14][15][16][17][18] are confronted with barriers found mainly on the basis of S and R compartments of SIR model due to pandemic complex scenario involving societies and SARS-CoV-2 distinct spreading patterns of infection where those two terms of equations are now suspended. "S2 figure"…”