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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>MLGH</title><link>https://mlglobalhealth.github.io/</link><atom:link href="https://mlglobalhealth.github.io/index.xml" rel="self" type="application/rss+xml"/><description>MLGH</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Mon, 18 Nov 2024 00:00:00 +0000</lastBuildDate><image><url>https://mlglobalhealth.github.io/media/icon_hu2550808fad87dd433d19be568a22a945_9051_512x512_fill_lanczos_center_3.png</url><title>MLGH</title><link>https://mlglobalhealth.github.io/</link></image><item><title>Modern Statistics and Machine Learning for Population Health in Africa</title><link>https://mlglobalhealth.github.io/short_courses/upcoming/aimsximperial2025/</link><pubDate>Mon, 18 Nov 2024 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/short_courses/upcoming/aimsximperial2025/</guid><description><style>
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<p><strong>When:</strong> 24th - 28th March 2025
<br>
<strong>Where:</strong> AIMS, Cape Town, South Africa</p>
<p>Imperial College London and AIMS South Africa are pleased to offer a new 1 week short course in Modern Statistics and Global Health. This course is targeted at Masters level students and above with a background in statistics and/or data science, who are interested in modern statistical methods with applications in global population health.</p>
<p>Attendees will learn how to:</p>
<ul>
<li>Write their own models in the probabilistic programming language Stan, carry out full Bayesian inference and assess model convergence,</li>
<li>Implement advanced statistical models including mixed effects models for hierarchical data, non-parametric gaussian process regression models and computationally scalable approximations,</li>
<li>Implement compartmental models for infectious disease epidemiology,</li>
<li>Implement a full phylogenetic analysis pipeline for the analysis of pathogen genomic data,</li>
<li>Interpret results and present findings</li>
</ul>
<p>Attendees will learn on the course through a blended mix of lectures, hands-on computer practicals and guided tutorials, group work and presenting to their peers.</p>
<p><strong>Pre-requisites:</strong> Good knowledge of Python and git</p>
<p><strong>Deadline to apply:</strong> 31 January 2025</p>
<center>
<a href="https://mlgh.net/sa_aimsximperial2025/overview/" class="button">More information<a/>
<a href="https://mlgh.net/sa_aimsximperial2025/application/" class="button">Apply<a/>
</center>
<br></description></item><item><title>AIMS-Imperial Rwanda 2024: Modern Statistics and Machine Learning for Global Health</title><link>https://mlglobalhealth.github.io/ms_ml_short_course/past_short_courses/aimsximperial2024/</link><pubDate>Thu, 22 Feb 2024 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/ms_ml_short_course/past_short_courses/aimsximperial2024/</guid><description><style>
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<p class="text">
In February 2024, 4 members of the Machine Learning and Global Health Network visited AIMS Rwanda to deliver a 3-day workshop on modern methods at the intersection of epidemiology, statistical modelling and computer science, including probabilistic programming using Stan, computationally efficient non-parametric Bayesian inference, statistical techniques for infectious disease modelling and public health, and phylogenetics.
<h2 id="course-contents">Course Contents</h2>
<p><em><strong>Thursday February 22</strong></em> </p>
<p>Lecture 1: Introductions (9.00-9.30) </p>
<p><strong>Bayesian Inference</strong> </p>
<p>Lecture 2: Introduction to Stan for applied Bayesian analyses (9.30-10.00) </p>
<p>Practical 1: Stan basics (10.00-11.30) </p>
<p>Lecture 3: Scalable Gaussian process regression models (11.45-12.30) </p>
<p>Practical 2: Scalable GP regression models (12.30-13.00) </p>
<p>Practical 2: Scalable GP regression models continued (14.00-16.00) </p>
<p><em><strong>Friday February 23</strong></em> </p>
<p><em><strong>Infectious Disease Modelling</strong></em> </p>
<p>Lecture 4: Introduction to Infectious Disease Modelling (09.00-10:00) </p>
<p>Lecture 5: Introduction to phylogenetics (10:00-10:40) </p>
<p>Lecture 6: (11.00-13:00) Four remote research talks from the Machine Learning and Global Health Network (Oliver Ratmann, Seth Flaxman, Samir Bhatt, Liza Semenova)</p>
<p>Practical 3: Running a phylogenetic pipeline (14:00-15:00) </p>
<p>Lecture 6: SIR models (15:00-16:00) </p>
<p><em><strong>Saturday February 24</strong></em> </p>
<p>Practical 4: Deriving SIR type models (09:00-10:30) </p>
<p>Lecture 7: Introduction to Fitting an SIR model practical (10:45-11:00) </p>
<p>Practical 5: Fitting an SIR model in Stan (11.00-13.00) </p>
<p><strong>Course material</strong>
All course material is freely available from <a href="https://github.com/MLGlobalHealth/aims_rwanda_2024" target="_blank" rel="noopener">https://github.com/MLGlobalHealth/aims_rwanda_2024</a></p>
<h2 id="course-testimonials-and-">Course testimonials and 📸</h2>
<p>A few quotes from the course:</p>
<p><em>“The course is very important. In this era of AI and Machine learning, it may be the time to train more youngsters and senior researchers to explore more about the use of AI in the field of modelling and treating communicable diseases.”</em></p>
<p><em>“I have been enjoying the content and learning Modern Statistics and Machine Learning for Global Health, methodology used was really valuable.”</em></p>
</p>
<center>
<img src="featured.jpg" width="1080"/>
<figcaption text-align: center></figcaption>
<img src="rwanda_course_1.jpg" width="1080"/>
<figcaption text-align: center></figcaption>
<img src="rwanda_course_2.png" width="1080"/>
<figcaption text-align: center></figcaption>
<img src="rwanda_course_3.jpg" width="1080"/>
<figcaption text-align: center></figcaption>
<img src="rwanda_course_4.png" width="1080"/>
<figcaption text-align: center></figcaption>
<img src="rwanda_course_5.png" width="1080"/>
<figcaption text-align: center></figcaption>
</center></description></item><item><title>Adding your profile</title><link>https://mlglobalhealth.github.io/add-profile/</link><pubDate>Fri, 02 Jun 2023 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/add-profile/</guid><description><ul>
<li>First thing is to fork the website <a href="https://github.com/MLGlobalHealth/starter-hugo-research-group" target="_blank" rel="noopener">repository</a></li>
<li>Clone the fork on your local machine. Let us assume the directory you have cloned is named <code>base_dir</code></li>
<li>Next is to install hugo for local development. You can find instructions at this <a href="https://gohugo.io/installation/" target="_blank" rel="noopener">page</a>. You may skip this page if you are not looking to have a local version, this will just not let you preview how you changes would look on the website.</li>
<li>Now go to the <code>base_dir</code> using the shell of you r choice on your local computer.
<figure id="figure-mac-os-sheel-after-cloning-the-repo">
<div class="d-flex justify-content-center">
<div class="w-100" ><img alt="Use Shell" srcset="
/media/add_person_1_hu30ee7c056da4af5447e6508d92951113_609297_1f15423fada40161595f484777d5231b.webp 400w,
/media/add_person_1_hu30ee7c056da4af5447e6508d92951113_609297_21fdbd12f712e4f0094c76ad980ec6f8.webp 760w,
/media/add_person_1_hu30ee7c056da4af5447e6508d92951113_609297_1200x1200_fit_q75_h2_lanczos_3.webp 1200w"
src="https://mlglobalhealth.github.io/media/add_person_1_hu30ee7c056da4af5447e6508d92951113_609297_1f15423fada40161595f484777d5231b.webp"
width="760"
height="168"
loading="lazy" data-zoomable /></div>
</div><figcaption>
Mac OS Sheel after cloning the repo.
</figcaption></figure>
</li>
<li>To add a new user run the following command <code>hugo new content/authors/firstname-lastname</code> . Remember to change first and last names as the name you want for yourself (surely you can be Swapnil and want to be known as Samir/Seth that can be benefecial :P )</li>
<li>This will create a new folder <code>firstname-lastname</code> inside <code>content/authors/</code></li>
<li>Now you can open the whole folder in a text editor of your choice and navigate to the file <code>content/authors/firstname-lastname/_index.md</code></li>
<li>Please fill in the details in this file as per your requirement. most of the stuff is self explanatory. In case of any issues raise a concern on zulip</li>
<li>Only thing to know is that in area with <code>user_groups</code> the allowed values are:
<ul>
<li>Principal Investigators</li>
<li>Researchers</li>
<li>Grad Students</li>
<li>Administration</li>
<li>Visitors</li>
<li>Alumni</li>
</ul>
</li>
<li>Delete <code>avatar.jpg</code> in your folder and replace with a file with pic you want to be displayed. Remember you need to have the name of the file as <code>avatar.jpg/avatar.png</code>.</li>
<li>This is how it look on vscode for reference
<figure id="figure-vscode-view-of-the-repo-and-added-new-folder">
<div class="d-flex justify-content-center">
<div class="w-100" ><img alt="Text Editor View" srcset="
/media/add_person_2_huea5de19e1e74e965e9dfc8be4c8d7fe0_289515_350e2768e5248e679b9df9ebd52b85de.webp 400w,
/media/add_person_2_huea5de19e1e74e965e9dfc8be4c8d7fe0_289515_e1dca6effd24bfc8b67c0b129fd5c453.webp 760w,
/media/add_person_2_huea5de19e1e74e965e9dfc8be4c8d7fe0_289515_1200x1200_fit_q75_h2_lanczos_3.webp 1200w"
src="https://mlglobalhealth.github.io/media/add_person_2_huea5de19e1e74e965e9dfc8be4c8d7fe0_289515_350e2768e5248e679b9df9ebd52b85de.webp"
width="760"
height="339"
loading="lazy" data-zoomable /></div>
</div><figcaption>
VSCODE view of the repo and added new folder.
</figcaption></figure>
</li>
<li>Now go back to terminal and to the <code>base_dir</code> and run <code>hugo serve</code>
<figure id="figure-view-of-the-terminal-after-running-hugo-serve">
<div class="d-flex justify-content-center">
<div class="w-100" ><img alt="Hugo Serve View" srcset="
/media/add_person_3_hu2d7fc37c4e015c8492356dffe70ba8d4_1628082_8c0236110c09d4b465d9168be2c16cc0.webp 400w,
/media/add_person_3_hu2d7fc37c4e015c8492356dffe70ba8d4_1628082_4bfa4bcc4038bbe9e715868f74a0c62f.webp 760w,
/media/add_person_3_hu2d7fc37c4e015c8492356dffe70ba8d4_1628082_1200x1200_fit_q75_h2_lanczos_3.webp 1200w"
src="https://mlglobalhealth.github.io/media/add_person_3_hu2d7fc37c4e015c8492356dffe70ba8d4_1628082_8c0236110c09d4b465d9168be2c16cc0.webp"
width="760"
height="308"
loading="lazy" data-zoomable /></div>
</div><figcaption>
View of the terminal after running hugo serve.
</figcaption></figure>
</li>
<li>This will display you changes on a localhost as informed on the terminal. Open that local host to see your changes
<figure id="figure-your-changes-on-the-local-host">
<div class="d-flex justify-content-center">
<div class="w-100" ><img alt="Changed website" srcset="
/media/add_person_4_hu84ed73dda82ef5a024ca2995a1353b4a_237322_14a2aff9d41b81802a9de73034fd8487.webp 400w,
/media/add_person_4_hu84ed73dda82ef5a024ca2995a1353b4a_237322_e219e7886758ea4599440daf173be72a.webp 760w,
/media/add_person_4_hu84ed73dda82ef5a024ca2995a1353b4a_237322_1200x1200_fit_q75_h2_lanczos_3.webp 1200w"
src="https://mlglobalhealth.github.io/media/add_person_4_hu84ed73dda82ef5a024ca2995a1353b4a_237322_14a2aff9d41b81802a9de73034fd8487.webp"
width="760"
height="561"
loading="lazy" data-zoomable /></div>
</div><figcaption>
Your changes on the local host.
</figcaption></figure>
</li>
<li>If you are happy then press <code>ctrl+c</code> to stop</li>
<li>Now delete the the public directory. The command for unix systems is <code>rm -rf public</code></li>
<li>Commit your changes and push it to your fork</li>
<li>Now submit a PR to the MLGH repository when happy</li>
<li>If you want to add other content than authors you can have a more detailed look at <a href="https://wowchemy.com/docs/getting-started/" target="_blank" rel="noopener">Wowchemy Documentation</a> for Hugo themes. Nothing more sophisticated than adding appropriate markdown files.</li>
</ul></description></item><item><title>πVAE: a stochastic process prior for Bayesian deep learning with MCMC</title><link>https://mlglobalhealth.github.io/publication/mishra-2022-oct/</link><pubDate>Sat, 01 Oct 2022 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/mishra-2022-oct/</guid><description/></item><item><title>Orphanhood and Caregiver Loss Among Children Based on New Global Excess COVID-19 Death Estimates</title><link>https://mlglobalhealth.github.io/publication/10-1001-jamapediatrics-2022-3157/</link><pubDate>Thu, 01 Sep 2022 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/10-1001-jamapediatrics-2022-3157/</guid><description/></item><item><title>PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation</title><link>https://mlglobalhealth.github.io/publication/semenova-2022-jun/</link><pubDate>Wed, 01 Jun 2022 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/semenova-2022-jun/</guid><description/></item><item><title>Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals</title><link>https://mlglobalhealth.github.io/publication/brizzi-2022-may/</link><pubDate>Sun, 01 May 2022 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/brizzi-2022-may/</guid><description/></item><item><title>A novel statistical framework for exploring the population dynamics and seasonality of mosquito populations</title><link>https://mlglobalhealth.github.io/publication/whittaker-2022-apr/</link><pubDate>Fri, 01 Apr 2022 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/whittaker-2022-apr/</guid><description/></item><item><title>Global, regional, and national minimum estimates of children affected by COVID-19-associated orphanhood and caregiver death, by age and family circumstance up to Oct 31, 2021: an updated modelling study</title><link>https://mlglobalhealth.github.io/publication/unwin-2022-apr/</link><pubDate>Fri, 01 Apr 2022 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/unwin-2022-apr/</guid><description/></item><item><title>Covid-19 is a leading cause of death in children and young people ages 0-19 years in the United States</title><link>https://mlglobalhealth.github.io/publication/flaxman-2022-covid/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/flaxman-2022-covid/</guid><description/></item><item><title>The use of Bayesian methodology in the development and validation of a tiered assessment approach towards prediction of rat acute oral toxicity</title><link>https://mlglobalhealth.github.io/publication/firman-2022-use/</link><pubDate>Sat, 01 Jan 2022 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/firman-2022-use/</guid><description/></item><item><title>Gaussian process nowcasting: application to COVID-19 mortality reporting</title><link>https://mlglobalhealth.github.io/publication/hawryluk-2021-dec/</link><pubDate>Wed, 01 Dec 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/hawryluk-2021-dec/</guid><description/></item><item><title>Unrepresentative big surveys significantly overestimated US vaccine uptake</title><link>https://mlglobalhealth.github.io/publication/bradley-2021-dec/</link><pubDate>Wed, 01 Dec 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/bradley-2021-dec/</guid><description/></item><item><title>Under-reporting of deaths limits our understanding of true burden of covid-19</title><link>https://mlglobalhealth.github.io/publication/whittaker-2021-oct/</link><pubDate>Fri, 01 Oct 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/whittaker-2021-oct/</guid><description/></item><item><title>Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe</title><link>https://mlglobalhealth.github.io/publication/sharma-2021-xj/</link><pubDate>Fri, 01 Oct 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/sharma-2021-xj/</guid><description/></item><item><title>Global minimum estimates of children affected by COVID-19-associated orphanhood and deaths of caregivers: a modelling study</title><link>https://mlglobalhealth.github.io/publication/hillis-2021-jul/</link><pubDate>Thu, 01 Jul 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/hillis-2021-jul/</guid><description/></item><item><title>Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions</title><link>https://mlglobalhealth.github.io/publication/smith-thomas-2021-jun/</link><pubDate>Tue, 01 Jun 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/smith-thomas-2021-jun/</guid><description/></item><item><title>Using Hawkes Processes to model imported and local malaria cases in near-elimination settings</title><link>https://mlglobalhealth.github.io/publication/unwin-2021-apr/</link><pubDate>Thu, 01 Apr 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/unwin-2021-apr/</guid><description/></item><item><title>Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England</title><link>https://mlglobalhealth.github.io/publication/volz-2021-mar/</link><pubDate>Mon, 01 Mar 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/volz-2021-mar/</guid><description/></item><item><title>Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK</title><link>https://mlglobalhealth.github.io/publication/du-plessis-2021-feb/</link><pubDate>Mon, 01 Feb 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/du-plessis-2021-feb/</guid><description/></item><item><title>Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence</title><link>https://mlglobalhealth.github.io/publication/sabino-2021-feb/</link><pubDate>Mon, 01 Feb 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/sabino-2021-feb/</guid><description/></item><item><title>Age groups that sustain resurging COVID-19 epidemics in the United States</title><link>https://mlglobalhealth.github.io/publication/monodeabe-8372/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/monodeabe-8372/</guid><description/></item><item><title>Asymptotic Equations of Gas Dynamics: Qualitative Analysis, Construction of Solutions, and Applications</title><link>https://mlglobalhealth.github.io/publication/vel-2021-asymptotic/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/vel-2021-asymptotic/</guid><description/></item><item><title>Bayesian workflow for disease transmission modeling in Stan</title><link>https://mlglobalhealth.github.io/publication/grinsztajn-2021-bayesian/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/grinsztajn-2021-bayesian/</guid><description/></item><item><title>Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England</title><link>https://mlglobalhealth.github.io/publication/mishra-2021-changing/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/mishra-2021-changing/</guid><description/></item><item><title>Flexible fitting of PROTAC concentration-response curves with changepoint Gaussian Processes</title><link>https://mlglobalhealth.github.io/publication/semenova-2021-flexible/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/semenova-2021-flexible/</guid><description/></item><item><title>Global disparities in SARS-CoV-2 genomic surveillance</title><link>https://mlglobalhealth.github.io/publication/brito-2021-global/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/brito-2021-global/</guid><description/></item><item><title>Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic</title><link>https://mlglobalhealth.github.io/publication/buss-2021-jan/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/buss-2021-jan/</guid><description/></item><item><title>Track Omicron’s spread with molecular data</title><link>https://mlglobalhealth.github.io/publication/scott-2021-track/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/scott-2021-track/</guid><description/></item><item><title>Uncertainty quantification in drug design</title><link>https://mlglobalhealth.github.io/publication/mervin-2021-uncertainty/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/mervin-2021-uncertainty/</guid><description/></item><item><title>Unifying incidence and prevalence under a time-varying general branching process</title><link>https://mlglobalhealth.github.io/publication/pakkanen-2021-unifying/</link><pubDate>Fri, 01 Jan 2021 00:00:00 +0000</pubDate><guid>https://mlglobalhealth.github.io/publication/pakkanen-2021-unifying/</guid><description/></item><item><title>State-level tracking of COVID-19 in the United States</title><link>https://mlglobalhealth.github.io/publication/unwin-2020-dec/</link><pubDate>Tue, 01 Dec 2020 00:00:00 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