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Quantifying the extent to which previous infections and vaccinations confer protection against future infection or disease outcomes is critical to managing the transmission and consequences of infectious diseases. We present a general statistical model for predicting the strength of protection conferred by different immunising exposures (numbers, types, and strains of both vaccines and infections), against multiple outcomes of interest, whilst accounting for immune waning.
The Vector Atlas aims to update and create vector species maps and spatial products that improve disease prediction, mitigation and preparedness.
A survey of 136 articles published in 2019 (sampled at random) was conducted to determine whether a statement about missing data was included.
The Infectious Disease Ecology and Modelling team led by Professor Nick Golding, combines mathematical and statistical modelling, ecology, and public health to address malaria and other infectious and vector-borne diseases. The team uses modelling and maps to measure the risk posed by some of the world’s most important and neglected diseases – including malaria, Japanese Encephalitis and COVID-19 – and provide rapid modelling analyses to policy makers.
The ACEFA NHMRC Centre of Research Excellence aims to support the timely, effective response to epidemic diseases in Australia through real-time data analytics, modelling, and forecasting.
Japanese Encephalitis virus is a mosquito-borne virus that is typically only found in south-east Asia.
Children are more vulnerable than adults to climate-related health threats, but reviews examining how climate change affects human health have been mainly descriptive and lack an assessment of the magnitude of health effects children face. This is the first systematic review and meta-analysis that identifies which climate-health relationships pose the greatest threats to children.
Disease surveillance data was critical in supporting public health decisions throughout the coronavirus disease 2019 (COVID-19) pandemic. At the same time, the unprecedented circumstances of the pandemic revealed many shortcomings of surveillance systems for viral respiratory pathogens. Strengthening of surveillance systems was identified as a priority for the recently established Australian Centre for Disease Control, which represents a critical opportunity to review pre-pandemic and pandemic surveillance practices, and to decide on future priorities, during both pandemic and inter-pandemic periods.
Following widespread exposure to Omicron variants, SARS-CoV-2 has transitioned to endemic circulation. Populations now have diverse infection and vaccination histories, resulting in heterogeneous immune landscapes. Careful consideration of the value of ongoing vaccination is required through the post-Omicron phase of COVID-19 management to minimise disease burden.
Contact tracing is an important public health measure used to reduce transmission of infectious diseases. Contact tracers typically conduct telephone interviews with cases to identify contacts and direct them to quarantine, with the aim of preventing onward transmission. However, in situations where caseloads exceed the capacity of the public health system, timely interviews may not be feasible for all cases. Here we present a modelling framework for assessing the impact of different case interview prioritisation strategies on disease transmission.