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Superspreading
Superspreading
Superspreaders play a key role in severe outbreaks, influenced by environmental, societal, and individual factors. Environmental aspects like climate and geography ease transmission, while age impacts susceptibility and contact diversity, especially among the very young and elderly. Beyond exposure numbers, who gets exposed also matters. Behavioral traits and adherence to interventions further shape spread dynamics. These interconnected factors highlight the need for models addressing multiple aspects of superspreaders, such as expanding compartmental models with patches, which combine network structures with additional complexities.
A schematic diagram showing environmental variability on superspreading disease transmission across variable populations
A schematic diagram showing environmental variability on superspreading disease transmission across variable populations
Data Analysis and Forecasting of COVID-19
Data Analysis and Forecasting of COVID-19
Although COVID-19 is no longer classified as a pandemic, localized infection increases persist, necessitating an understanding of transmission dynamics and regional impacts for public health planning. Using data from health agencies, international organizations, and research institutions, the study applies statistical and machine learning techniques to explore the spread of COVID-19. Temporal and spatial patterns of incidence, prevalence, and mortality are examined with descriptive statistics, time series analysis, and spatial mapping to identify hotspots and disparities. Predictive models, including ARIMA and machine learning algorithms, forecast disease trajectories and assess the effectiveness of interventions. Data is extracted using SQL in BigQuery, visualized in Looker Studio, and analyzed primarily in RStudio.
Biofilm-Phage Dynamics
Biofilm-Phage Dynamics
When bacteria adhere to a surface, they form biofilms, which are resistant to antibiotics and pose serious health risks, such as in cystic fibrosis. Phage therapy, which uses bacteriophages to target bacteria, offers a promising alternative. We developed a phage-biofilm model using both deterministic approaches and and stochastic simulations to evaluate therapy effectiveness.
Schematic representation and stochastic modeling of Biofilm-Phage dynamics in Cystic Fibrosis
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