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Predicting the Outbreak Risks and Inflection Points of COVID-19 Pandemic with Classic Ecological Theories.

ABSTRACT: Predicting the outbreak risks and/or the inflection (turning or tipping) points of COVID-19 can be rather challenging. Here, it is addressed by modeling and simulation approaches guided by classic ecological theories and by treating the COVID-19 pandemic as a metapopulation dynamics problem. Three classic ecological theories are harnessed, including TPL (Taylor's power-law) and Ma's population aggregation critical density (PACD) for spatiotemporal aggregation/stability scaling, approximating virus metapopulation dynamics with Hubbell's neutral theory, and Ma's diversity-time relationship adapted for the infection-time relationship. Fisher-Information for detecting critical transitions and tipping points are also attempted. It is discovered that: (i) TPL aggregation/stability scaling parameter (b > 2), being significantly higher than the b-values of most macrobial and microbial species including SARS, may interpret the chaotic pandemic of COVID-19. (ii) The infection aggregation critical threshold (M ₀) adapted from PACD varies with time (outbreak-stage), space (region) and public-health interventions. Exceeding M ₀, local contagions may become aggregated and connected regionally, leading to epidemic/pandemic. (iii) The ratio of fundamental dispersal to contagion numbers can gauge the relative importance between local contagions vs. regional migrations in spreading infections. (iv) The inflection (turning) points, pair of maximal infection number and corresponding time, are successfully predicted in more than 80% of Chinese provinces and 68 countries worldwide, with a precision >80% generally.

SUBMITTER: Ma ZS

PROVIDER: S-EPMC7536942 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe pressure-volume (P-V) curve has been suggested as a bedside tool to set mechanical ventilation; however, it reflects a global behavior of the lung without giving information on the regional mechanical properties. Regional P-V (PVr) curves derived from electrical impedance tomography (EIT) could provide valuable clinical information at bedside, being able to explore the regional mechanics of the lung. In the present study, we hypothesized that regional P-V curves would provide different information from those obtained from global P-V curves, both in terms of upper and lower inflection points. Therefore, we constructed pressure-volume curves for each pixel row from non-dependent to dependent lung regions of patients affected by acute hypoxemic respiratory failure (AHRF) and acute respiratory distress syndrome (ARDS).MethodsWe analyzed slow-inflation P-V maneuvers data from 12 mechanically ventilated patients. During the inflation, the pneumotachograph was used to record flow and airway pressure while the EIT signals were recorded digitally. From each maneuver, global respiratory system P-V curve (PVg) and PVr curves were obtained, each one corresponding to a pixel row within the EIT image. PVg and PVr curves were fitted using a sigmoidal equation, and the upper (UIP) and lower (LIP) inflection points for each curve were mathematically identified; LIP and UIP from PVg were respectively called LIPg and UIPg. From each measurement, the highest regional LIP (LIPrMAX) and the lowest regional UIP (UIPrMIN) were identified and the pressure difference between those two points was defined as linear driving pressure (ΔPLIN).ResultsA significant difference (p < 0.001) was found between LIPrMAX (15.8 [9.2-21.1] cmH2O) and LIPg (2.9 [2.2-8.9] cmH2O); in all measurements, the LIPrMAX was higher than the corresponding LIPg. We found a significant difference (p < 0.005) between UIPrMIN (30.1 [23.5-37.6] cmH2O) and UIPg (40.5 [34.2-45] cmH2O), the UIPrMIN always being lower than the corresponding UIPg. Median ΔPLIN was 12.6 [7.4-20.8] cmH2O and in 56% of cases was < 14 cmH2O.ConclusionsRegional inflection points derived by EIT show high variability reflecting lung heterogeneity. Regional P-V curves obtained by EIT could convey more sensitive information than global lung mechanics on the pressures within which all lung regions express linear compliance.Trial registrationClinicaltrials.gov, NCT02907840 . Registered on 20 September 2016.

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Predicting the Outbreak Risks and Inflection Points of COVID-19 Pandemic with Classic Ecological Theories.

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