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The spatial distribution of dengue and its vectors (spp. Aedes ) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, less work has been done to understand how climate variability and change affects dengue in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatiotemporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last 30 years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronisation events. We show that although patterns in synchrony of dengue are similar to those observed in temperature, the relationship between the two is most consistent during synchronous periods, while during asynchronous periods, temperature plays a less prominent role. With simulations from temperature-driven models, we explore how dynamics of immunity interact with temperature to produce the observed patterns in synchrony. The simulations produced patterns in synchrony that were similar to observations, supporting an important role of immunity. We demonstrate that multiannual oscillations produced by immunity can lead to asynchronous dynamics and that synchrony in temperature can then synchronise these dengue dynamics. At higher mean temperatures, immune dynamics can be more predominant, and dengue dynamics more insensitive to multiannual fluctuations in temperature, suggesting that with rising mean temperatures, dengue dynamics may become increasingly asynchronous. These findings can help underpin predictions of disease patterns as global temperatures rise.

Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data. We found that Omicron had higher R 0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections. This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

Introduction The first detected case in Lebanon on 21 February 2020 engendered implementation of a nationwide lockdown alongside timely contact-tracing and testing. Objectives Our study aims to calculate the serial interval of SARS-CoV-2 using contact tracing data collected 21 February to 30 June 2020 in Lebanon to guide testing strategies. Methods rRT-PCR positive COVID-19 cases reported to the Ministry of Public Health Epidemiological Surveillance Program (ESU-MOH) are rapidly investigated and identified contacts tested. Positive cases and contacts assigned into chains of transmission during the study time-period were verified to identify those symptomatic, with non-missing date-of-onset and reported source of exposure. Selected cases were classified in infector–infectee pairs. We calculated mean and standard deviation for the serial interval and best distribution fit using AIC criterion. Results Of a total 1788 positive cases reported, we included 103 pairs belonging to 24 chains of transmissions. Most cases were Lebanese (98%) and male (63%). All infectees acquired infection locally. Mean serial interval was 5.24 days, with a standard deviation of 3.96 and a range of − 4 to 16 days. Normal distribution was an acceptable fit for our non-truncated data. Conclusion Timely investigation and social restriction measures limited recall and reporting biases. Pre-symptomatic transmission up to 4 days prior to symptoms onset was documented among close contacts. Our SI estimates, in line with international literature, provided crucial information that fed into national contact tracing measures. Our study, demonstrating the value of contact-tracing data for evidence-based response planning, can help inform national responses in other countries.

•MMR vaccine uptake rates have dropped between 25.64%−73.55% during COVID-19.•A measles epidemic is a possibility due to reduction in community herd immunity.•Urgent public health efforts are needed to maintain efficacious vaccine coverage. Although the direct health impact of Coronavirus disease (COVID-19) pandemic on child health is low, there are indirect impacts across many aspects. We compare childhood vaccine uptake in three types of healthcare facilities in Singapore - public primary care clinics, a hospital paediatric unit, and private paediatrician clinics - from January to April 2020, to baseline, and calculate the impact on herd immunity for measles. We find a 25.6% to 73.6% drop in Measles-Mumps-Rubella (MMR) uptake rates, 0.4 – 10.3% drop for Diphtheria-Tetanus-Pertussis-inactivated Polio-Haemophilus influenza (5-in-1), and 8.0–67.8% drop for Pneumococcal conjugate vaccine (PCV) across all 3 sites. Consequent herd immunity reduces to 74–84% among 12-month- to 2-year-olds, well below the 95% coverage that is protective for measles. This puts the whole community at risk for a measles epidemic. Public health efforts are urgently needed to maintain efficacious coverage for routine childhood vaccines during the COVID-19 pandemic.

Dengue is a vector-borne disease found across much of the world, with an increasing number of cases annually. This thesis explores the dynamics of dengue infection within an individual, and the possible impact of this at a population level. I use mathematical modelling and statistical analysis, tightly coupled with data, as a way of tying together the important components and processes during infection. I model the virus and immune dynamics, capturing the differences between individuals, disease severity and primary/secondary disease (with a focus on hypothesised secondary mechanisms). Within the immune dynamics I concentrate on antibody, looking at the role of antibody in limiting infection. Within this framework I also consider the impact on these dynamics of an antiviral. The final section of this thesis brings together this closer consideration of virus dynamics and considers their impact at a population level. Using data from biting experiments I am able to characterise the “infectivity” of an individual over time, how this varies between individuals and groups (as above), and how this compares to previous transmission modelling assumptions. In terms of control I look at how this “infectivity” is altered by antivirals and by wolbachia infected mosquitoes.

Abstract The spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, the largest impacts of climate change on dengue might be in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatio-temporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last thirty years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronization events. We show that patterns in synchrony of dengue are consistent with those observed in temperature. Applying a temperature-driven dengue model, we explore how dynamics of immunity interact with temperature to produce the observed multiannual dynamics and patterns in synchrony. While multiannual oscillations are readily produced by immunity in absence of multiannual timescales in temperature, synchrony in temperature can synchronise dengue dynamics in different locations. However, at higher mean temperatures and lower seasonal variation, immune dynamics become more predominant, and dengue dynamics become more insensitive to multiannual fluctuations in temperature. These findings can help underpin predictions of disease patterns as global temperatures rise.