Trends In Spread Research Articles

Spatial expansion characteristics of rural settlements and its response to determinants in Hangzhou Bay Urban Agglomeration, China: Geospatial modeling using multiscale geographically weighted regression (MGWR)

The expansion of rural settlements has become a significant issue in China's progress toward rural revitalization. Understanding the intricate connections between rural settlement expansion and its determinants is crucial. Previous studies have focused on identifying the characteristics and factors that contribute to rural settlement expansion at a fixed study scale, whereas the spatial heterogeneity of the impact of key drivers on rural settlement expansion at different geographical scales has been neglected. The spatial heterogeneity response relationship of driving factors to rural settlement expansion is a key basis for formulating management strategies; however, few studies have focused on this aspect. To address this knowledge gap, we constructed the Intensity–Space–Pattern–Driving research framework and investigated layout changes in rural settlement expansion and the complex spatial non-stationarity relationships between rural settlement expansion and various factors. Taking the Hangzhou Bay Urban Agglomeration as a case study, this study reveals that rural settlements rapidly expanded, which was primarily characterized by edge expansion from 1980 to 2020. Rural settlement expansion exhibited significant spatial heterogeneity across various regions, as evidenced by the shifting center of gravity of expansion, which moved from the northeast to the central region. Furthermore, the expansion direction indicated a trend of spreading from coastal areas to inland regions. These variations were closely linked to factors such as the natural environment, socio-economic conditions, geographic location, and others. Among the factors affecting the expansion of rural settlements, elevation, fractional vegetation cover (FVC) change, distance to factories, road density, and distance to city centers had the most significant response to the spatial heterogeneity of rural settlement expansion, meaning that they presented different directions and thresholds of influence at different spatial scales. This paper quantitatively assessed the impact of each driver at multiple geographic scales and proposed differentiated strategies for optimizing rural settlement layouts according to local conditions. This study reveals the specific characteristics of rural settlement expansion in rapidly urbanizing areas, provides a new analytical framework for a deeper understanding of the spatial heterogeneity of influencing factors, and offers a scientific basis for rural planning and management.

Projecting the Potential Global Distribution of Sweetgum Inscriber, Acanthotomicus suncei (Coleoptera: Curculionidae: Scolytinae) Concerning the Host Liquidambar styraciflua Under Climate Change Scenarios.

Acanthotomicus suncei is a newly discovered bark beetle in China that significantly threatens the American sweetgum Liquidambar styraciflua. In recent years, this pest has spread from its original habitat to many surrounding cities, causing substantial economic and ecological losses. Considering the wide global distribution of its host, Liquidambar styraciflua, this pest is likely to continue to spread and expand. Once the pest colonizes a new climatically suitable area, the consequences could be severe. Therefore, we employed the CLIMEX and Random Forests model to predict the potential suitable distribution of A. suncei globally. The results showed that A. suncei was mainly distributed in Southern China, in South Hokkaido in Japan, Southern USA, the La Plata Plain in South America, southeastern Australia, and the northern Mediterranean; these areas are located in subtropical monsoon, monsoonal humid climates, or Mediterranean climate zones. Seasonal rainfall, especially in winter, is a key environmental factor that affects the suitable distribution of A. suncei. Under future climates, the total suitable area of A. suncei is projected to decrease to a certain extent. However, changes in its original habitat require serious attention. We found that A. suncei exhibited a spreading trend in Southwest, Central, and Northeast China. Suitable areas in some countries in Southeast and South Asia bordering China are also expected to show an increased distribution. The outward spread of this pest via sea transportation cannot be ignored. Hence, quarantine efforts should be concentrated in high-suitability regions determined in this study to protect against the occurrence of hosts that may contain A. suncei, thereby avoiding its long-distance spread. Long-term sentinel surveillance and control measures should be carried out as soon as A. suncei is detected, especially in regions with high suitability. Thus, our findings establish a theoretical foundation for quarantine and control measures targeting A. suncei.

The Coupled Application of the DB-IWHR Model and the MIKE 21 Model for the Assessment of Dam Failure Risk

The phenomenon of global climate change has led to an increase in the frequency of extreme precipitation events, an acceleration in the melting of glaciers and snow cover, and an elevation of the risk of flooding. In this study, the DB-IWHR model was employed in conjunction with the MIKE 21 hydrodynamic model to develop a simulation system for the dam failure flow process of an earth and rock dam. The study concentrated on the KET reservoir, and 12 dam failure scenarios were devised based on varying design flood criteria. The impact of reservoir failures on flood-risk areas was subjected to detailed analysis, with consideration given to a range of potential failure scenarios and flood sizes. It was determined that under identical inflow frequency conditions, the higher the water level, the more rapid the breakout process and the corresponding increase in flood peak discharge. Conversely, for a given frequency of incoming water, an elevated water level results in a transient breach process, accompanied by a reduction in flood peak flow. Moreover, for a given water level, an increase in water frequency results in a reduction in breaching time, an extension of flood duration, and an increase in flood peak flow. The observed trend of flood spreading is generally north-south, and this process is highly compatible with the topographic and geomorphological features, demonstrating good adaptability.

Beijing Central Asian Outbreak strain cluster of Mycobacterium tuberculosis detected in Irkutsk region, Russia

Background. Irkutsk region maintains a high incidence rate of multidrug-resistant (MDR) tuberculosis (TB). Detection of MDR-associated Mycobacterium tuberculosis strains in Irkutsk region requires dynamic assessment of the TB pathogen population, taking into account the emergence of a new resistant variant of Beijing Central Asian Outbreak (CAO).The aim of the study. To assess changes in the genotypic structure of M. tuberculosis strains circulating in the Irkutsk region over a ten-year period.Materials and methods. A total of 732 M. tuberculosis strains (196 strains for 2021– 2022, 536 strains for 2011–2015) were studied using MIRU-VNTR and SNP typing.Results. The MDR level increased to 67.4 % with an increase of pre-extensive drug resistance (pre-XDR) (33.2 %) (p < 0.001). In the modern sample, the dominance of the Beijing genotype increased (83.7 %) due to an increase in the proportions of the B0/W148 (38.8 %) and CAO (12.8 %) subtypes with a stable overall level of Central Asian Russian (36.8 %) and other Beijing strains (8.2 %). Strains other than the Beijing genotype belonged mainly to the Euro-American lineage (Lineage 4): LAM (8.9 %), Ural (2.7 %), Haarlem (2.0 %), S (0.5 %) and L4-unclassified (5.3 %); 25 isolates were not classified. In the 2011–2015 sample, LAM was more common than in the modern sample (10.8 % vs. 3.6 %; p < 0.01). The increase in MDR and preXDR was statistically significant among Beijing B0/W148 strains (93.4 % vs. 66.1 %; p < 0.001).Conclusion. Unfavorable trends of significant spread of MDR and pre-XDR of the Beijing genotype strains were revealed. Among the Beijing strains, not only B0/W148 but also the Beijing CAO subtype, which was previously rare in Siberia, are the most successful; they have the highest levels of MDR and pre-XDR and a tendency to widespread distribution in all groups of TB patients.

A stacking-based model for the spread of Botryosphaeria laricina

Botryosphaeria laricina (larch shoot blight) was first identified in 1973 in Jilin Province, China. The disease spread rapidly and caused considerable damage because its pathogenesis was unknown at the time and there were no effective controls or quarantine methods. At present, it shows a spreading trend, but most research can only conduct physiological analyses within a relatively short period, combining individual influencing factors. Nevertheless, methods such as neural network models, ensemble learning algorithms, and Markov models are used in pest and disease prediction and forecasting. However, there may be fitting issues or inherent limitations associated with these methods. This study obtained B. laricina data at the county level from 2003 to 2021. The dataset was augmented using the SMOTE algorithm, and then algorithms such as XGBoost were used to select the significant features from a combined set of 12 features. A new stacking fusion model has been proposed to predict the status of B. laricina. The model is based on random forest, gradient boosted decision tree, CatBoost and logistic regression algorithms. The accuracy, recall, specificity, precision, F1 value and AUC of the model reached 90.9%, 91.6%, 90.4%, 88.8%, 90.2% and 96.2%. The results provide evidence of the strong performance and stability of the model. B. laricina is mainly found in the northeast and this study indicates that it is spreading northwest. Reasonable means should be used promptly to prevent further damage and spread.

Long-term trend prediction of pandemic combining the compartmental and deep learning models

Predicting the spread trends of a pandemic is crucial, but long-term prediction remains challenging due to complex relationships among disease spread stages and preventive policies. To address this issue, we propose a novel approach that utilizes data augmentation techniques, compartmental model features, and disease preventive policies. We also use a breakpoint detection method to divide the disease spread into distinct stages and weight these stages using a self-attention mechanism to account for variations in virus transmission capabilities. Finally, we introduce a long-term spread trend prediction model for infectious diseases based on a bi-directional gated recurrent unit network. To evaluate the effectiveness of our model, we conducted experiments using public datasets, focusing on the prediction of COVID-19 cases in four countries over a period of 210 days. Experiments shown that the Adjust-R2 index of our model exceeds 0.9914, outperforming existing models. Furthermore, our model reduces the mean absolute error by 0.85–4.52% compared to other models. Our combined approach of using both the compartmental and deep learning models provides valuable insights into the dynamics of disease spread.

The marginal effect of landscapes on urban land surface temperature within local climate zones based on optimal landscape scale

The rising urban land surface temperature (LST) has undoubtedly caused an imminent threat to human habitat. Landscape patterns are among the most significant factors related to LST. However, the marginal effects and thresholds of landscape at optimal grid scale on LST within diverse local climate zones (LCZs) are poorly understood. This study employed the boosted regression trees (BRT) model to evaluate the relative influences and marginal effects of built-up, water, and green landscape metrics on LST at the optimal grid scale across various LCZs. The findings reveal that: (1) The spatial spread trend of LST closely matches the expansion trend of built-up landscapes. (2) Significant variations in average summer LST were observed among LCZs, with LCZ8 (large low-rise) exhibiting the highest temperatures and LCZ11 (dense trees) the lowest. (3) At the optimal scale of 1000 m, the DIVISION metric for water landscapes significantly influenced LCZ6 (open low-rise), LCZ8, and LCZ14 (low plants). (4) In LCZ6 and LCZ8, effective heat mitigation requires increasing water area to 50% and optimizing the water body separation index to 0.88. These findings suggest that heterogeneous landscape heat mitigation strategies should be considered across diverse LCZs in urban areas.

Epidemiological Model of the Regional Distribution and Analysis of Hepatitis B in Ghana: A Sub-Saharan African Country

Hepatitis B (HB) is a global public health burden which has impacted economies of many countries, particularly in sub-Saharan Africa. The effect is worse where there is lack of definite information regarding policy direction and capacity towards implementing interventions to control the transmission and spread of the infection. In this study, a SEIR deterministic compartmental model and a hybrid optimisation method for parameter estimation, similar to that of Awuah-Mensah et al. (2022), were used to analyse the regional distribution of HB in Ghana, a sub-Saharan African country. The regional epidemiological threshold parameters [Formula: see text] and herd immunity threshold parameters [Formula: see text] computed based on the estimated parameters for each of the regions ranged from a minimum of [Formula: see text] for Volta region in the southeast to a maximum of [Formula: see text] for Upper West region in the northwest of Ghana. These values of [Formula: see text] and [Formula: see text] indicated that HB persists in Ghana and the transmission and spread and the associated burden were found to generally increase from south to north of the country. Based on the estimated parameters, only Western region recorded a recovery rate of [Formula: see text]. The remaining nine regions recorded zero recovery rates. The trend of transmission and spread of the infection was also found to be positively related to poverty and health sector inequalities across the regions of the country.

Fire behavior simulation of Xintian forest fire in 2022 using WRF-fire model

IntroductionThe behavior of forest fire is a complex phenomenon, and accurate simulation of forest fire is conducive to emergency response management after ignition. In order to further understand the characteristics of forest fire spread and the applicability of WRF-Fire in China, which is a coupled fire-atmospheric wildfire model, this study simulated a high-intensity forest fire event that occurred on October 17, 2022 in Xintian County, southern Hunan Province.MethodsBased on the fire-atmosphere coupled WRF-Fire model, we used high-resolution geographic information, meteorological observation and fuel classification data to analyze the forest fire behavior. At the same time, the simulation results are compared with the fire burned area observed by satellite remote sensing forest fire monitoring data.ResultsThe study found that, the simulated wind speed, direction and temperature trends are similar to the observation results, but the simulated wind speed is overestimated, the dominant wind direction is N, and the temperature is slightly underestimated. The simulated wind field is close to the actual wind field, and the simulation results can show the spatial and temporal variation characteristics of the local wind field under complex terrain while obtaining the high-resolution wind field. The simulated fire burned area is generally overestimated, spreading to the north and southwest compared with the observed fires, but it can also capture the overall shape and spread trend of the fire well.DiscussionThe results show that the model can accurately reproduce the real spread of fire, and it is more helpful to forest fire management.

High-performance real-time fire detection and forecasting framework for industrial cables

In industrial scenarios, cable fires have always been the most common threat, and traditional fire detection systems often rely on a large number of sensors, and the detection range is very limited, and it is impossible to effectively predict the fire situation in time. In this paper, we propose a detection and prediction scheme for industrial cable fire, which breaks the limitations of the previous research on multi-sensor signal input, and highly couples the detection and prediction modules to realize fire prediction based on video image input only. In fire detection, we design an object detection model using HSV for flame feature enhancement based on YOLOv8, and in fire prediction aspect, we use iTransformer as a time series prediction model to mine the correlation between various parameters to predict the spread of fire. In the experiments, the average absolute percentage error of the flame detection model for the detection of flame height, width and longitudinal position was 3.49%–10.64 %, 2.45%–8.89 % and 1.61%–9.31 %, respectively, and the MAPE of the time series prediction model for the above three parameters was 11.18%–15.06 % and 4.35%–8.18 %, 3.37%–6.62 %.The results of the above experiments verify that the proposed model has the ability to quantitatively analyze the fire spread trend in the actual fire and help firefighters make decisions.

Evolution of regional rainstorm events in China's South-to-North Water Diversion Area, 1960–2022

The objective of this paper is to elucidate the patterns of evolution of regional rainstorm events (RREs) in a large-scale water resources allocation area. This is crucial for the safe operation of water transfer projects and water resources management. The Objective Identification Technique for Regional Extreme Events (OITREE) method is employed to identify all RREs in the South-to-North Water Diversion Area (SNWDA) between the years 1960 and 2022. The results indicate that among the 1397 RREs identified, the extreme RREs are significantly more severe than other classes of rainstorm events. While the extreme intensity (Im) of RREs exhibits a decreasing trend, their frequency, comprehensive index (Z), maximum area (Am), summed area (As), and summed intensity (Is) all demonstrate increasing trends. Some of these indices (Z, Am, As, and Is) exhibit a strong periodicity of approximately 20 years, while the shortening of the decline period will result in more frequent rising periods of RREs. In the second year following the water transfer operation (2015), all RRE indices exhibit abrupt changes. Furthermore, the rainy season lengthens, with more frequent and extreme RREs occurring in July, which may have implications for the Middle Route of year-round water transfers. Spatially, the occurrence frequency of regional rainstorm days (RRDs) increases by 35.9% since the 21st century, while regional rainstorm intensity (RRI) increases by 11.8%. The high-frequency zones (HFZs) of RRDs are primarily located in the southeast, with intensity increases of at least 30%. While their location remains stable, there is a clear trend of northward spread. Following the water transfer, the frequency of RREs is higher in the Eastern Route, and the high-intensity zones (HIZs) appear for the first time in the Beijing area of the Middle Route. These findings provide a scientific understanding of regional rainstorms and serve as a reference for rainstorm disaster response and optimal allocation of water resources in water transfer projects.

Covid-19 Review: A Trend Analysis of the Impact of Religious Festivities and Non-Adherence to Social Distancing in Nigeria

The global negative impact of Covid-19 pandemic necessitates curtailing measures and policies in case of future outbreak. A trend analysis of the spread of Covid-19 showing the effect of social gatherings especially during religious festivals was done. Covid-19 monthly spread and associated weather data from Nigerian Centre for Disease Control (NCDC) for the period of March 2020 to April 2022 was used to study the spread trend during religious festivities. The trend analysis revealed a significant spread increase for four of the geopolitical zones at r = 0.023, 0.000, 0.000, 0.000 respectively during religious festivals. A consistent increase in the spread of the disease during religious festivals across the six zones and a downward trend immediately after the festivities. A t test value of -29.027 was significant at p-value of 0.000 confirming that the change in spread trend during festivals was not a chance occurrence

Dispersal patterns and potential distribution prediction of three rice planthopper species in China based on the ensemble model

AbstractThree rice planthopper species, Nilaparvata lugens, Sogatella furcifera and Laodelphax striatellus, are destructive rice pests that cause serious economic losses in major rice‐producing regions of China. Understanding their dispersal patterns and potential distribution areas is crucial for effective management and control. Based on historical climate data and simulated future climate data combined with species occurrence data, the dispersal patterns of three planthopper species were analysed and their potential distributions were predicted. The results showed that from 1993 to 2022, the suitable areas for the three rice planthoppers gradually expanded. By 2022, the total suitable area for S. furcifera reached 478.35 × 104 km2, followed by 376.02 × 104 km2 for L. striatellus and 264.97 × 104 km2 for N. lugens. The optimum area for S. furcifera exhibited the most significant increase, expanding by 136.07 × 104 km2. The species has extended its range from southern China to the northeastern and northwestern regions within the past three decades. The suitable areas for L. striatellus and N. lugens did not show a significant spread trend. In addition, compared to that in 2022, the total suitable area for the three rice planthopper species increased slightly from 2041 to 2060. The study revealed that the wettest monthly rainfall and the lowest temperature were the most influential factors affecting the distributions of these planthopper species. This study provides valuable insights into the dispersal patterns and potential distribution of rice planthoppers in China and provides a theoretical basis for effective prevention and control strategies for destructive rice pests.

Revealing the urban heat exposure risk network: Exploring the possibility of mitigate heat-related risks form a network perspective

The exposure of urban residents to heat waves is expected to increase in the future. However, the analyses regarding the spread characteristics and trends of heat exposure risk in different cities are lacking. In this study, we proposed a framework to identify the network spread characteristics of heat exposure risk based on morphological spatial pattern analysis and applied it to 33 provincial capital cities in China. We explored the patterns and characteristics of heat exposure risk diffusion at different urbanization stages and for different urban sizes and in response to socioeconomic factors. The results show that network spread is common in all cities. From 2000 to 2020, heat exposure risk exhibited a strengthening diffusion trend, and line to Surface accounted for the highest proportion and area. There are great differences in the degree of heat exposure risk expansion in different cities. Urban size determines the speed of expansion in heat exposure risk, and the proportion of risk areas indicates that some medium-sized cities, such as Haikou and Lanzhou warrant greater research attention. Socioeconomic factors, notably GDP, positively influence risk expansion, but their impact weakens over time and with urban size growth. The present research results provide evidence for the spread characteristics of heat exposure risk, and provide a new perspective for mitigating the heat-related risk.

Modeling the spread of rice blast in rice plant group based on cellular automata

Studying and modeling the process of the disease and spread of blast can guide fertilization and reduce the amount of pesticide application. Since most of the existing research focuses on the pathogenesis of the rice blast fungus and the interaction between the plant structure and the fungus, this paper presents a method to simulate the spread of rice blast among rice populations. The method is divided into the following processes. First, based on the cellular automata grid dynamics model, the cell state corresponds to the degree of infection of the rice blast fungus, and its ability to simulate the spatiotemporal evolution of complex systems is used to simulate the spread of disease spots on the surface of rice leaves. Second, according to the biological mechanism of the rice blast fungus and the position of the first infected leaf in a single rice plant, the normal distribution function is used to calculate the infection probability of the remaining leaves, and then the transmission mode of the disease inside a single rice plant is simulated. Then, in view of the natural phenomenon that the blast fungus spores spread with the wind, the wind factor is added to propose a method for the spread of the rice blast fungus among the population of rice plants, and thus a model for the spread of the pathogen among the entire plant population was established. Finally, we set up a simulation system for the spread of rice blasts in rice fields and visualized the spread and spread trend of rice blasts in rice populations. In the simulation experiment, the infection process of rice blast on the surface of the rice leaf was simulated, and the change process of the shape of the diseased spot can be visually seen. This paper not only provides a new idea for the research on pathogen propagation and diffusion between plants in agricultural production but also provides a reference for how to realistically visualize the pathogen propagation model in the virtual agricultural scene.

Forest Biological Disaster Control Behaviors of Forest Farmers and Their Spatial Heterogeneity in China

With more and more loss caused by forest biological disasters (FBDs) in China, forest farmers, as one of the most important stakeholders, are participating in the control. In this study, the ordinary least squares model, simultaneous equation model, and propensity score matching method were used with the data from 818 surveys conducted in the typical FBD outbreak provinces, to reveal the FBD control behaviors of forest farmers and their differences between western and eastern regions of China. The results indicated the following. (1) Household factors: forest farmers could timely take control measures. An increase of 1 ha in the area of occurrence would increase the control measures by 3.26 ha. However, the control measures can only reduce 50% of the economic loss caused by FBDs and cannot effectively control the spread trend of FBDs. There are issues, including an insufficient and old labor force, insufficient technology support, and low consciousness of ecology protection. (2) External factors: forest farmers would increase control when the temperature rises, and reduce control when rainfall increases. After village committees unify organizing the control, their participation enthusiasm would increase, which would have a substitution relationship with the household investment. (3) Regional difference: the eastern region of China has higher figures than the western in terms of outbreak area, economic losses, control measures, and pesticide cost. If the western forest farmers have the control funds as the eastern forest farmers have, and the eastern forest farmers have the control intensity as the western forest farmers have, the overall FBD control effect would be better.

Research of the Spread of Covid-19 in World Wide and Particular Countries

To understand the spread of COVID-19, the data sets from Google and GitHub are obtained by using Python and R, and ARIMA model is established for prediction. By visualising the data sets, it can be found that the total trend of the COVID-19 is bimodal and reaches its peaks during the winter and spring time in 2022 and 2023, and this may due to the virus activity and government management. When taking into prediction, ARIMA (3,1,3) is the best predict model to forecast the overall spread trend of COVID-19 as the data is not normal distributed since it keeps fluctuating in the P-P plot, and it can be considered as time series, showing a seasonal pattern. This conclusion is drawn by calculating the values of AIC and BIC to see which model has smallest. At the same time, the ACF and PACF index could determine the p and q value in the model. The model can help to predict the spread of similar diseases.

Diffusion and the Abolition of the Death Penalty: A Global Comparison

ABSTRACT We identify four patterns of diffusion that are likely to explain how trends in death penalty usage spread among countries. The first is geographic proximity, which creates dependency relations between nations that in turn affect the spread of changes in death penalty regimes. The second is global diffusion, meaning processes that spread ideas globally. The third is cultural homogeneity, which can be expected to be conducive for diffusion; studies show that countries similar in terms of language and religion influence each other to a great extent. Finally, similar colonial heritage, which can establish cultural and institutional ties between countries. This study encompasses all independent countries of the world over the period 1800 to 2021. In addition to measures of diffusion, a number of control variables are introduced. Relationships are measured using statistical techniques complemented by qualitative analyses.

Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach

The COVID-19 pandemic has significantly impacted global health, social, and economic situations since its emergence in December 2019. The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach, concentrating on the year 2021. We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis (EDA) approach. While no vaccine guarantees total immunity against the disease, and vaccine immunity wanes over time, it is critical to include and accurately estimate vaccine efficacy, as well as a constant vaccine immunity decay or wane factor, to better simulate the dynamics of vaccine-induced protection over time. Based on the distribution and effectiveness of vaccines, we integrated a data-driven estimation of vaccine efficacy, calculated at 75% for Malaysia, underscoring the model's realism and relevance to the specific context of the country. The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters. The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy. Our findings reveal that this distinct vaccination policy, which emphasizes an accelerated vaccination rate during the initial stages of the program, is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections. The study found that vaccinating 57–66% of the population (as opposed to 76% in the real data) with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections. The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination, offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies, particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy. While the methodology used in this study is specifically applied to national data from Malaysia, its successful application to local regions within Malaysia, such as Selangor and Johor, indicates its adaptability and potential for broader application. This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes, implying its usefulness for similar datasets from various geographical regions.

Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks

A physics-informed neural network (PINN) embedded with the susceptible–infected–removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible–asymptomatic–infected–recovered–dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.