H2020Индивидуална стипендия2020–2022

EstAMR · Estimating the Prevalence of AntiMicrobial Resistance

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-04-01 → 2022-07-31
Финансиране от ЕС: 203 149 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Разпространението на антимикробната резистентност се проследява чрез модел, който открива конкретни места (например болници), откъдето се разпространяват устойчиви щамове на бактерии. Това помага да се установи дали причините са ниско качество на лекарствата или неправилно приемане на лечението.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Estimating the Prevalence of AntiMicrobial Resistance

To prevent drug resistance, treatment needs to be effective and the full prescribed course of treatment needs to be completed. However, this can be difficult to monitor, especially in low to middle income countries where health care access may be limited. For example, perhaps a treatment centre is using low quality drugs, which is known to promote drug resistance occurring and spreading. This model allows these treatment centres to be identified, whilst accounting for uncertainty in the data collection, and varying disease prevalence over the region. To demonstrate the main model of my project, consider a toy example in Square Land over 20 years (see figures). Within Square Land you have sampled infected people, at different locations and times, and identified whether they carry a resistant strain or not. In addition, suppose you know the location of five treatment access points, such as health care centres or hospitals (however knowledge of the exact locations is not a requirement of the model). There is data on infected people in Square Land for 20 years. Some patients are carrying a sensitive strain, and some are carrying a resistant. We know the location and time that people were tested, and that there is more disease prevalence at the bottom of Square Land. The model created here identifies which hotspot is introducing more resistant infections into the population. In this example, the model identifies that the top left hotspot (hotspot 1) is contributing the most resistant infection into the population. Therefore, if hotspot 1 was a health care centre, it would be worth investigating the quality of the drugs administered here and/or the adherence of the patients. If this hotspot was a transport hub, it would inform us that drug resistance is entering the population from outside. The middle hotspot (hotspot 3) has more resistant infections in the region (due to higher prevalence of malaria cases here), however because the model explicitly accounts for the higher prevalence of malaria (not necessarily drug resistant malaria) at the bottom of Square Land, it stills successfully identifies hotspot 1. Analysis without a mechanistic component, as presented here, would incorrectly suggest investing hotspot 3, not hotspot 1.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Antimicrobial resistance (AMR) is the ability of an infection to stop antimicrobials, such as antibiotics, antivirals and antimalarials, from working against it. In the EU, every year AMR is responsible for thousands of deaths and costs millions of euros. Yet forecasting the prevalence of AMR remains an open challenge. This project meets this challenge by developing cutting-edge Hierarchical Bayesian models (HBMs). As a case study, the models will focus on the malaria parasite Plasmodium falciparum that has developed resistance to sulfadoxine/pyrimethamine between 1994 and 2016. This ensures that the goals are realistic, with immediate insight and impact, whilst also remaining methodologically relevant for all AMR. Within the field of ecology, regression models are being replaced so as to incorporate more complex non-linear relationships between abundance and the environment. Unlike traditional methods, HBMs are spatiotemporal models that (i) account for varying geography (ii) separate underlying processes and (iii) include uncertainty in the data and model parameters. This thorough package has proven to yield more insight and accuracy. By using partial differential equations, HBMs separate occurrence due to spread and occurrence due to emergence. Despite its relevance, differentiating between these two process is currently unexplored in epidemiology. Furthermore, when modelling AMR, infections competing for hosts is a process which is currently unexplored in HBMs. Thus, the two long term contributions of this project are: Bringing HBMs to epidemiology to gain a better understanding of underlying processes, and advancing the field of HBMs to include more complex dynamics. And more immediately, the two key contributions are: Quantifying the dynamics and influencers of the spread of drug resistant malaria, and forecasting the frequency of partially drug resistant malaria, and fully drug resistant malaria, at different locations and at different times.

Оригинален текст от CORDIS (на английски).

Участници

SCHWEIZERISCHES TROPEN UND PUBLIC HEALTH INSTITUT · AllschwilКоординаторШвейцария