Rapid estimation of an earthquake impact area using a spatial logistic growth model based on social media data

Rapid estimates of impact areas following large earthquakes constitute the cornerstone of emergency response scenarios.However,collecting information through traditional practices usually requires a large amount of manpower and material resources,slowing the response time.Social media has emerged as a source of real-time‘citizen-sensor data’for disasters and can thus contribute to the rapid acquisition of disaster information.This paper proposes an approach to quickly estimate the impact area following a large earthquake via social media.Specifically,a spatial logistic growth model(SLGM)is proposed to describe the spatial growth of citizen-sensor data influenced by the earthquake impact strength after an earthquake;a framework is then developed to estimate the earthquake impact area by combining social media data and other auxiliary data based on the SLGM.The reliability of our approach is demonstrated in two earthquake cases by comparing the detected areas with official intensity maps,and the time sensitivity of the social media data in the SLGM is discussed.The results illustrate that our approach can effectively estimate the earthquake impact area.We verify the external validity of our model across other earthquake events and provide further insights into extracting more valuable earthquake information using social media.

Monitoring the impact of Movement Control Order (MCO) in flattening the cummulative daily cases curve of Covid-19 in Malaysia: A generalized logistic growth modeling approach

Introduction:COVID-19 has affected almost every country in the world,which causing many negative implications in terms of education,economy and mental health.Worryingly,the trend of second or third wave of the pandemic has been noted in multiple regions despite early success of flattening the curve,such as in the case of Malaysia,post Sabah state election in September 2020.Hence,it is imperative to predict ongoing trend of COVID-19 to assist crucial policymaking in curbing the transmission.Method:Generalized logistic growth modelling(GLM)approach was adopted to make prediction of growth of cases according to each state in Malaysia.The data was obtained from official Ministry of Health Malaysia daily report,starting from 26 September 2020 until 1 January 2021.Result:Sabah,Johor,Selangor and Kuala Lumpur are predicted to exceed 10,000 cumulative cases by 2 February 2021.Nationally,the growth factor has been shown to range between 0.25 to a peak of 3.1 throughout the current Movement Control Order(MCO).The growth factor range for Sabah ranged from 1.00 to 1.25,while Selangor,the state which has the highest case,has a mean growth factor ranging from 1.22 to 1.52.The highest growth rates reported were inWP Labuan for the time periods of 22 Nov-5 Dec 2020 with growth rates of 4.77.States with higher population densities were predicted to have higher cases of COVID-19.Conclusion:GLM is helpful to provide governments and policymakers with accurate and helpful forecasts on magnitude of epidemic and peak time.This forecast could assist government in devising short-and long-term plan to tackle the ongoing pandemic.

A Novel Parameter-Free Filled Function and Its Application in Least Square Method

The filled function algorithm is an important method to solve global optimization problems.In this paper,a parameter-free filled function is proposed for solving general global optimization problem,discuss the theoretical properties of this function and give the corresponding algorithm.The numerical experiments on some typical test problems using the algorithm and the numerical results show that the algorithm is effective.Applying the filled function method to the parameter solving problem in the logical population growth model,and then can be effectively applied to Chinese population prediction.The experimental results show that the algorithm has good practicability in practical application.

Adequacy of Logistic models for describing the dynamics of COVID-19 pandemic

Logistic models have been widely used for modelling the ongoing COVID-19 pandemic.This study used the data for Kuwait to assess the adequacy of the two most commonly used logistic models(Verhulst and Richards models)for describing the dynamics COVID-19.Specifically,the study assessed the predictive performance of these two models and the practical identifiability of their parameters.Two model calibration approaches were adopted.In the first approach,all the data was used to fit the models as per the heuristic model fitting method.In the second approach,only the first half of the data was used for calibrating the models,while the other half was left for validating the models.Analysis of the obtained calibration and validation results have indicated that parameters of the two models cannot be identified with high certainty from COVID-19 data.Further,the models shown to have structural problems as they could not predict reasonably the validation data.Therefore,they should not be used for long-term predictions of COVID-19.Suggestion have been made for improving the performances of the models.

A new approach of proration-injection allocation for water-flooding mature oilfields

This paper presents a new method of injection-production allocation estimation for water-flooding mature oilfields.The suggested approach is based on logistic growth rate functions and several type-curve matching methods.Using the relationship between these equations,oil production and water injection rate as well as injection-production ratio can be easily forecasted.The calculation procedure developed and outlined in this paper requires very few production data and is easily implemented.Furthermore,an oilfield case has been analyzed.The synthetic and field cases validate the calculation procedure,so it can be accurately used in forecasting production data,and it is important to optimize the whole injection-production system.