The extra fields can be used for data to pass to that custom function. Sometimes it will be defined in stonehearth/jobs/base_job.lua instead, since all jobs inherit from it. This "type" is the name of a function defined in the job's controller file.
#Acemod health calculator plus#
If the perk involves something more elaborate, it will have a "type" field plus any additional custom fields for data. If the perk consists in something that is automatically controlled by other files (such as unlocking recipes - each recipe defines at which level it will get unlocked) no extra fields are needed. The "id" is an identifier used to perform checks in Lua when we need to know whether a hearthling has unlocked a perk or not. The minimum fields are "name", "id", "icon", "description" and "level". They all consist of an array of JSON objects. The way job perks from the job_description.json file work is: E.g.: "controllers": - the list of job perks for this job. The existing jobs are inside the stonehearth/jobs directory.Īdd an alias for your custom job in your manifest, pointing to the JSON file that contains the description of the job.Īdd an alias in the "controllers" section of your manifest, pointing to the Lua file of your job. Preferably one that is most close in functionality to what you're creating. These are generic steps for creating a new job:Ĭopy an existing job from the stonehearth mod. The study presents a strong argument for a large-scale vaccination campaign in Australia, which would substantially reduce both the intensity of future outbreaks and the stringency of non-pharmaceutical interventions required for their suppression.Īustralian Research Council National Health and Medical Research Council.We can create our own jobs / classes and alter the existing promotion tree to suit our preferences. The severity of epidemics, as measured by the peak number of daily new cases, decreases by up to two orders of magnitude under plausible mass-vaccination and lockdown strategies. In our simulations, Australia’s vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43% and 52%, respectively. For realistic scenarios in which herd immunity is not achieved, we simulate the effects of mass-vaccination on epidemic growth rate, and investigate the requirements of lockdown measures applied to curb subsequent outbreaks.
Within a feasible range of vaccine efficacy values, our model supports the assertion that complete herd immunity due to vaccination is not likely in the Australian context. The model is calibrated to recent epidemiological and demographic data available in Australia, and accounts for several components of vaccine efficacy. We apply a large-scale agent-based model of COVID-19 in Australia to investigate the possible implications of this hybrid approach to mass-vaccination. The rest of the population will instead have access to a less effective vaccine. To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine.
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