Emerging and re-emerging infectious disease outbreaks have continued to cause loss or damage to life. These diseases caused many life-threatening consequences worldwide. Many human-made and environmental factors drive the infection and severity of these outbreaks. Interventions were taken to reduce the transmission and deaths. One of the critical questions is how useful these measures are. In this thesis, I assessed the impacts of the non-pharmaceuticals interventions (NPIs) on the transmission and severity of newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In addition, as environmental factors such as weather conditions (short-term conditions of the atmosphere) and air pollution also influence the transmission and severity of SARS-CoV-2, I assessed the impacts of these environmental factors on the disease outcomes. Moreover, I explored the impact of climate (the average daily weather for an extended period of time at a particular location) variability on another persistently growing tropical infectious disease, dengue.

During the early coronavirus disease 2019 (COVID-19) phases, many reported cases were associated with travel history from an epidemic region. The imported cases and their secondary ones (i.e., people infected by the imported cases) can pose different risks to community spread. Therefore, the study of this importation risk is essential for predicting the spread of the virus. In Chapter 2, I investigated the risk of the disease spread under quarantine and border control using the meta-population susceptible, infected, and recovered (SIR) model. I observed that the border control and quarantine measures are more effective in slowing the community spread when the virus's transmissibility is comparatively lower. However, if the outbreak at the source region is not contained and the virus transmissibility is high, only ten extra days can be gained for each of the same measures. The results imply that reducing the number of passengers (even 90%) is insufficient to prevent the global spread and suggests the importance of minimizing the incidence at source city and infectious disease control measures in susceptible areas.

Transmissibility and disease severity of SARS-CoV-2 are linked with viral load. Maintaining sufficient health care capacity or effective hospital treatment is likely to reduce case fatality rate (CFR). However, in Chapter 3, using the data in the United States, I observed that a higher CFR occurred when a higher percentage of infected cases are hospitalized. This controversy enables me to hypothesize whether CFR was driven by other factors. A possible mechanism is that hospital isolation affects the evolution of SARS-CoV-2 severity.

When a host's viral load is higher, both infectivity and severity are likely to be enhanced. Once a host becomes ill or develops certain symptoms, he/she might visit a hospital. Patients are more likely to go to a hospital and isolated if their symptoms are more severe. From evolutionary perspective, a more virulent virus strain which can cause higher severity in patients then becomes "short-sighted" with lower chance to produce offspring. Therefore, a trade-off between disease spread and severity can exist. I developed evolutionary and transmission models to observe viral load dynamics within the host and the disease transmissibility in the population in Chapter 3. I observed that the effect of hospitalization on CFR occurred one and a half month later, suggesting that evolutionary process plays an important role on the change in CFR.

Understanding the impact of environmental factors, such as fine particular matter (PM_2.5) and sunlight exposure, on the morbidity and mortality rates of COVID-19 can facilitate an improvement in its management. In the United Kingdom (UK), national death rates from COVID-19 have varied across its constituent countries during the lockdown period. This provides a unique opportunity to assess the impact of these environmental factors on the probability that a newly infected individual will eventually die (i.e., CFR) during this period when health care capacity is not overwhelmed. In Chapter 4, I developed a generalized linear mixed-effect model combined with distributed lag nonlinear models to quantify these effects. Lower sunlight duration and increased PM_2.5 during the average incubation period and after symptom onset posed a greater risk of death. The odds ratio was high throughout and after the incubation period when the PM_2.5 ranged between 10 and 18 μg/m³. Sunlight duration of 5-7 hours (h) was associated with a higher odds ratio, which referred to the higher risk of death than the reference sunlight duration of 9h.

In addition, I studied whether we are able to predict annual dengue incidence using early weather data before the dengue epidemic begins in Bangladesh in Chapter 5. The incidence of dengue has increased rapidly since 2010, with an outbreak in 2018 reaching a historically high number of cases, 10,148. A better understanding of the effects of climate variability during the months before dengue season on the increasing incidence of dengue in Bangladesh enables the development of early warning systems for future outbreaks. I developed a generalized linear model to predict the number of annual dengue cases based on monthly minimum temperature, rainfall, and sunlight duration before dengue season. Our model successfully predicted the largest outbreak in 2018, with 10,077 cases (95% CI: [9,912-10,276]). I found that temperature was positively associated with the annual incidence during the late winter months (between January and March) but negatively associated during the early summer (between April and June).

Overall, the studies conducted in this thesis would help to understand the impact of interventions taken and the weather conditions on two major infectious diseases, COVID-19 and dengue. Policymakers and the general public may take initiatives to overcome the crises caused by these diseases based on the results of this thesis.