Evaluation of spatial spreading of phyto-available sulphur and micronutrients in cultivated coastal soils

Understanding the spatial spreading patterns of plant-available sulphur (S) (AS) and plant-available micronutrients (available zinc (AZn), available iron (AFe), available copper (ACu), available manganese (AMn) and available boron (AB)) in soils, especially in coastal agricultural soils subjected to various natural and anthropogenic activities, is vital for sustainable crop production by adopting site-specific nutrient management (SSNM) strategies. We studied the spatial distribution patterns of AS, AZn, AFe, ACu, AMn, and AB in cultivated soils of coastal districts of India using geostatistical approaches. Altogether 39,097 soil samples from surface (0 to 15 cm depth) layers were gathered from farm lands of 68 coastal districts. The analysis of soil samples was carried out for soil pH, electrical conductivity (EC), soil organic carbon (SOC) and AS, AZn, AFe, ACu, AMn, and AB. Soil pH, EC and SOC varied from 3.70 to 9.90, 0.01 to 7.45 dS m-1 and 0.02 to 3.74%, respectively. The concentrations of AS, AZn, AFe, ACu, AMn, and AB varied widely in the study area with their corresponding mean values were 37.4±29.4, 1.50±1.53, 27.9±35.1, 2.14±1.74, 16.9±18.4 and 1.34±1.52 mg kg-1, respectively. The coefficient of variation values of analyzed soil parameters varied from 14.6 to 126%. The concentrations of AS, AZn, AFe, ACu, AMn, and AB were negatively and significantly correlated with soil pH and positively and significantly correlated with SOC. The geostatistical analysis indicated stable, Gaussian and exponential best-fit semivariogram models with moderate to strong spatial dependence for available nutrients. The generated spatial spreading maps revealed different distribution patterns for AS, AZn, AFe, ACu, AMn, and AB. There were variations in spatial spreading patterns of AS, AZn, AFe, ACu, AMn, and AB in east- and west-coastal area. About 62, 35, 12, 0.4, 23 and 45% of the study area had deficiency of AS, AZn, AFe, ACu, AMn, and AB, respectively. The spatial spreading maps will be highly useful for SSNM in the cultivated coastal soils of the country. This study could also be used as a base for assessing spatial spreading patterns of soil parameters in cultivated coastal areas of other parts of the world.

Epidemiological model for the inhomogeneous spatial spreading of COVID-19 and other diseases

We suggest a novel mathematical framework for the in-homogeneous spatial spreading of an infectious disease in human population, with particular attention to COVID-19. Common epidemiological models, e.g., the well-known susceptible-exposed-infectious-recovered (SEIR) model, implicitly assume uniform (random) encounters between the infectious and susceptible sub-populations, resulting in homogeneous spatial distributions. However, in human population, especially under different levels of mobility restrictions, this assumption is likely to fail. Splitting the geographic region under study into areal nodes, and assuming infection kinetics within nodes and between nearest-neighbor nodes, we arrive into a continuous, “reaction-diffusion”, spatial model. To account for COVID-19, the model includes five different sub-populations, in which the infectious sub-population is split into pre-symptomatic and symptomatic. Our model accounts for the spreading evolution of infectious population domains from initial epicenters, leading to different regimes of sub-exponential (e.g., power-law) growth. Importantly, we also account for the variable geographic density of the population, that can strongly enhance or suppress infection spreading. For instance, we show how weakly infected regions surrounding a densely populated area can cause rapid migration of the infection towards the populated area. Predicted infection “heat-maps” show remarkable similarity to publicly available heat-maps, e.g., from South Carolina. We further demonstrate how localized lockdown/quarantine conditions can slow down the spreading of disease from epicenters. Application of our model in different countries can provide a useful predictive tool for the authorities, in particular, for planning strong lockdown measures in localized areas—such as those underway in a few countries.

Levels of economic growth and cross-province spread of the COVID-19 in China

BackgroundAfter the first COVID-19 case detected on 8 December 2019 in Wuhan, the Provincial Capital of Hubei, the epidemic quickly spread throughout the whole country of China. Low developmental levels are often associated with infectious disease epidemic, this study attempted to test this notion with COVID-19 data.MethodsData by province from 8 December 2019 to 16 February 2020 were analysed using regression method. Outcomes were days from the first COVID-19 case in the origin of Hubei Province to the date when case was first detected in a destination province, and cumulative number of confirmed cases. Provincial gross domestic products (GDPs) were used to predict the outcomes while considering spatial distance and population density.ResultsOf the total 70 548 COVID-19 cases in all 31 provinces, 58 182 (82.5%) were detected in Hubei and 12 366 (17.5%) in other destination provinces. Regression analysis of data from the 30 provinces indicated that GDP was negatively associated with days of virus spreading (β=−0.2950, p<0.10) and positively associated with cumulative cases (β=97.8709, p<0.01) after controlling for spatial distance. The relationships were reversed with β=0.1287 (p<0.01) for days and β=−54.3756 (p<0.01) for cumulative cases after weighing in population density and controlling for spatial distance.ConclusionHigher levels of developmental is a risk factor for cross-province spread of COVID-19. This study adds new data to literature regarding the role of economic growth in facilitating spatial spreading of infectious diseases, and provides timely data informing antiepidemic strategies and developmental plan to balance economic growth and people’s health.

Comments on “EpiRank: Modeling Bidirectional Disease Spread in Asymmetric Commuting Networks” for Analyzing Emerging Coronavirus Epidemic Patterns

ABSTRACTHuang et al.1 used their EpiRank algorithm, which emphasizes forward-and-backward commuter flow between homes and workplaces, to analyze the distribution patterns of two infectious diseases in Taiwan: the 2009-H1N1 influenza virus and the widespread emergence of the 2000-2008 type 71 enterovirus (EV). As this article was being prepared, the spreading mechanism of the novel coronavirus disease now designated as COVID-19 had yet to be identified, but according to the American Centers for Disease Control, its spreading mechanism and patterns are likely more similar to influenza than to other coronaviruses such as Severe Acute Respiratory Syndrome (SARS-CoV-1) or Middle East Respiratory Syndrome (MERS-CoV). To consider potential COVID-19 spatial patterns, we applied EpiRank to the 2003 SARS outbreak in north Taiwan for comparison with H1N1 and EV. SARS was found to be less contagious than H1N1 or EV, but with a significantly higher fatality rate. The characteristics of these diseases determined their specific spatial spreading patterns, as reflected in the different effects of forward and backward commuting movement. Our motivation is to highlight these differences and to illustrate EpiRank spatial patterns for the 2003 SARS outbreak for comparison with EpiRank-determined distributions for the H1N1 and EV outbreaks. Our results indicate that the daytime parameter (i.e., forward movement effect) range was slightly higher (0.5-0.55) for the SARS outbreak than for either the influenza (0.4-0.5) or EV (0.3-0.5) outbreaks, suggesting that the forward-and-backward movements of individuals between residential and core urban areas with concentrated populations were equally important regarding the spread of SARS. While COVID-19 might resemble either SARS or H1N1 in terms of spatial spreading, its daytime parameter is likely somewhere in-between, with backward movement being dominant (similar to H1N1) or with forward and backward movement being equally important (similar to SARS). Building on Huang et al.’s paper, we present an estimated risk distribution pattern for the Taipei Metropolitan Area for a daytime parameter of 0.55.