DMC 2026

Abstract Submission Guidelines & Awards

Important Dates

Call for abstracts open

15th January 2026 (Thursday)

Abstract submission deadline

15th April 2026 (Wednesday)

Notification of Acceptance

1st June 2026 (Sunday)

Abstract Awards

There will be awards for the 3 best posters and oral presentations. Cash prizes and certificates will be given to the winners. Successful presenters will be notified during the conference (closing ceremony).

Oral Presentations

Prize Cash Prize
1st Prize RM 1,000
2nd Prize RM 700

Poster Presentations

Prize Cash Prize
1st Prize RM 750
2nd Prize RM 500

Free registration will be given to the 5 best Oral presentations and 5 best Poster presentations.

Guidelines for Abstract Submission

Abstract Format

General Requirements:

  • Abstract must be in **Microsoft Word format**.
  • Graphs, tables and illustrations **cannot be included** in the abstract.
  • The abstract should be fitted into **one single page**.
  • Use **Times New Roman font, size 12**, with **1.15 line spacing** and **justified alignment**.
  • Abstract should be **less than 250 words**.

Title, Author & Affiliation:

  • **Title:** Must be **Bolded and Centred**, use **title case capitalization** (capitalize the first word, a word following a colon, and any significant words. Do not capitalize articles, prepositions, or conjunctions). Written in the top-half of the page.
  • Place **two double-spaced lines** after the end of the title.
  • **Author(s) Name(s):** List authors in order by which they contributed to the work.
  • **Affiliation:** On the line following the author’s name, write the name of the department or program and the name of the University. Include this information for each author.

Structured Abstract Content:

  • Write the section label **“Abstract” in bold title case, centred** at the top of the page.
  • The abstract should be a **single paragraph divided into 5 structured parts**:
    1. Introduction
    2. Objectives
    3. Methods
    4. Results
    5. Conclusion
  • Write the label **“Keywords:”** (in italic) one line below the abstract, indented 0.5 in., followed by the keywords in lowercase (capitalize proper nouns), separated by commas. Keywords should be between 3 to 5 words.

Example Abstract Format

Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia

Cia Vei Tan1, Sarbhan Singh1, Chee Herng Lai1, Ahmed Syahmi Syafiq Md Zamri1, Sarat Chandra Dass2, Tahir Bin Aris1, Hishamshah Mohd Ibrahim3, Balvinder Singh Gill1

1 Institute for Medical Research (IMR), Ministry of Health Malaysia, Shah Alam 40170, Malaysia

2 School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya 62200, Malaysia

3 Ministry of Health, Malaysia, Putrajaya 62590, Malaysia

Abstract

Introduction: With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. Objective: This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. Method: SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia’s official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. Results: The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Conclusion: Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.

Keywords: COVID-19, forecast, ARIMA, Malaysia

(220 words)