With the first COVID-19 epidemic peak behind them, many countries explained the decrease of infection numbers through non-pharmaceutical interventions.
Phrases like “social distancing” and “flatten the curve” have become part of common vocabulary.
Yet some explanations fell short: How could one explain the linear rise of infection curves, which many countries display after the first peak, in contrast to the S-shaped curves, expected from epidemiological models?
In a new paper published in the Proceedings of the National Academy of Sciences, scientists at the Complexity Science Hub Vienna (CSH) offer an explanation for the linear growth of the infection curve.
“At the beginning of the pandemic, COVID-19 infection curves showed the expected exponential growth,” says Stefan Thurner, CSH president and professor for Science of Complex Systems at the Medical University of Vienna.
This can be well explained by a so-called snowball effect: An infected person would infect a few others, and in a chain reaction, those would pass the virus on to a few others, as well.
“With measures like social distancing, governments tried to push the growth rate below the recovery rate and therefore massively reduce the number of new infections.
In this logic, however, individuals would have infected less than one other person, and the curve would have flattened, eventually reaching zero—something that did not happen,” explains Thurner.
“What we saw instead was a constant level of infections with a similar number of new infections every day,” adds co-author Peter Klimek (CSH & Medical Univ of Vienna). “To explain this with standard epidemiological models would basically be impossible.”
The use of traditional epidemiological models would have required a lot of fine-tuning of parameters, making the model increasingly implausible.
“If you want to balance measurements so that the effective reproduction number R stays exactly at 1 – something that would explain the linear growth – you would have to reduce contacts by the same exact and constant percentage. In reality that is extremely unlikely,” says Klimek.
In fact, the probability to observe linear growth in these standard compartmental models is practically zero, the CSH scientists point out. They were therefore inspired to extend the model and look for further explanations.
The scientists explained the linear shape of the curves through a different form of spreading than initially expected: They assumed that the spreading dynamic continued in small and limited clusters.
“Most people went to work, got infected and spread it to two or three people at home, and then those people went to work or school again. The infection was basically spreading from cluster to cluster,” says Stefan Thurner.
“The change of the infection curves from being S-shaped to a linear behavior is clearly a network effect—a dynamic very different from big superspreading events.”
The scientists showed that there is a critical number of contacts, which they call degree of contact networks or Dc, below which linear growth and low infection prevalence must occur.
They found Dc to equal 7.2, assuming that people circulate in a coronavirus-relevant network of about five people, which is even lower during an effective lockdown (household-size 2.5 people on average).
Instead of having to fine-tune parameters, their model allows for a wide range of possibilities that keep the infection curves linear. It explains why linear infection curves appear in so many countries, irrespective of the magnitude of the imposed non-pharmaceutical interventions.
In a further step the scientists compared Austria, a country which responded with a severe lockdown early on, and the United States, which initially did not impose severe measures. According to Peter Klimek, their model works for both scenarios: “Both types of countries showed linear curves, but in the case of the US and other countries like Sweden, these just happened on a much higher level.”
The model not only explains the emergence of a linear growth regime, but also explains why the epidemic could halt below the levels of herd immunity by consequent social distancing. For the standard modeling procedure, the complexity scientists use a so-called compartmental model with SIR-models, extending it with the described cluster transmission.
But what will happen in the next months, with the potential of numbers rising again? With additional risk factors like people returning from vacation in other countries and more time spent inside, the spread of disease could change. “If infections rise again, there is the potential that linear curves turn to exponential growth again—something people described as a second wave,” Klimek concludes.
Coronavirus (COVID-19), a highly transmittable disease that threatens the human population worldwide, is considered to be the third pandemic within the 21st century (Perlman, 2020). After the emergence of Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia and severe acute respiratory syndrome coronavirus (SARS-CoV) in China, a new SARS-like coronavirus has been discovered last December 2019 (Zhou et al. 2020).
The virus was linked to a wholesale market for seafood in Wuhan, China, when unknown pathogenesis was identified by the local hospitals in a group of patients who are accustomed to pneumonia (Zhu et al. 2019; Fan et al. 2020). Adnan Shereen et al. (2020) initially mentioned that patients who have been infected by the coronavirus in China might have gone to Wuhan where live animals were being sold.
Furthermore, Chinese researchers have determined a human to human transmission of the virus and initially named the novel virus as Wuhan coronavirus or 2019 novel coronavirus (2019-nCoV) (Adam, 2020).
The International Committee on Taxonomy of Viruses (ICTV) recently named the novel coronavirus into SARS coronavirus 2 (SARS-COV-2), which is now generally called coronavirus disease 19 (COVID-19) (Gorbalenya et al., 2020).
Details about this epidemic was released by the National Health Commission of China on January 12th, 2020 (Wang et al., 2020). This virus outbreak has grown extensively to infect 13,575,158 people worldwide and lead to 584,940 death cases as of July 17th 2020 (WHO, 2020). On March 11th 2020, the World Health Organization listed COVID-19 as a global pandemic.
Due to the global pandemic of COVID-19, several prevention measures were taken. A massive lockdown has been implemented by governments in several countries.
In addition, “#StayAtHome” was also promoted by media to prevent the spread of COVID-19. Moreover, researchers are currently in the process of developing a vaccine and there is no effective medicine that has invented for the medication of COVID-19 infections (Paital et al., 2020).
At the moment, the only possible remedial option is hospitalization and thorough care unit management. With these initial medications, predicting the duration and final size of the virus in every country become very critical for policymakers and public health authorities in preventing the spread of the outbreak.
Despite the availability of different studies about the preventive measures in other countries, there is a significant lack of academic research addressing the COVID-19 situation in the Philippines. On July 16th 2020, Philippines was declared as the highest number of active cases in Southeast Asia (Table 1 ).
On March 17th 2020, the country has placed under community quarantine for six months (CSIS, 2020). The Department of Health of the Philippines (DoH) reported there had been 61,266 confirmed cases of the infection and 1,643 deaths in the Philippines as of July 17th, 2020 (Figure 1 ).
COVID-19 Cases in Southeast Asia as of July 17th, 2020 (https://www.worldometers.info/coronavirus/).
|Rank||Country||Total Cases||New Cases||Total Deaths||New Deaths||Total Recovered||Active Cases|
On March 16th 2020, The Philippine government imposed a total lockdown in Luzon which widely known as Enhanced Community Quarantine (ECQ) as a preventive measure to minimize the COVID-19 outbreak. Under the ECQ, all modes of domestic travel, including ground, air, and seas shall be suspended.
The residents were not allowed to leave their homes except in case of emergencies. Border closures and entry bans are also enforced. Thousands of police officers and military personnel have been deployed in checkpoints to ensure that the people are complying with the lockdown.
The administration has also implemented strict social distancing (Duddu, 2020, CSIS, 2020) and educates healthy lifestyles to the community through several platforms as preventive measures.
The purpose of this study was to evaluate factors affecting the perceived effectiveness of COVID-19 prevention measures among Filipino during ECQ in Luzon, Philippines by integrating Protection Motivation Theory (PMT) and extended Theory of Planned Behavior (TPB).
The current study is one of the first studies that analyzed factors affecting perceived effectiveness of COVID-19 prevention measures during the global pandemic in 2020.
Finally, the integrated PMT and extended TPB of this study can be applied and extended to evaluate the perceived effectiveness of COVID-19 measures in other countries that currently dealing with COVID -19 pandemic.
Theoretical Research Framework
Figure 2 represents the Theoretical Research Framework of the current study. Different from one previous study which only utilized PMT related to COVID-19 (Barati et al., 2020), we integrated the PMT and extended TPB to determine the causal relationships between determined variables and latent assembles.
The final goal was to evaluate factors affecting the perceived effectiveness of COVID-19 prevention measures among Filipino during ECQ in Luzon.
Based on PMT, when individuals encounter a threatening event, they are most motivated to engage in protective behavior (Janmaimool, 2017).
According to studies, individuals believe that performing preventive behavior can reduce the threat that comes with the lack of action (Hung et al. 2014). During the global crisis, providing reliable and accurate information is very essential.
In 2009, the public believed that H1N1 virus was much more lethal than H5N1 human avian flu and SARS because various forms of new infectious diseases were merged together (Lau et al. 2009). Early knowledge of the outbreak can help to illustrate the public’s risk behavior and how they perceived it (Balkhy et al. 2010).
Moreover, Johnson and Hariharan (2017) highlighted that during an outbreak, providing health education and creating awareness is an effective measure to prevent the spread of the disease. Thus, we hypothesized the following:
H1. Understanding of COVID-19 had a significant direct effect on perceived vulnerability.
H2. Understanding of COVID-19 had a significant direct effect on perceived severity.
Health understanding and perceived behavioral control have been identified frequently as predictors to an individuals’ tendency to follow the prescribed prescription which subsequently conforms to treatment procedures (Davis et al. 2006). Bandura (1977) stated that the essential predictor of various medical behaviors including medication constancy is the perceived behavioral control. However, Cameron et al. (2010) point out that taking medication as prescribed is the responsibility of the patient. It is not an unusual occurrence if, due to inadequate knowledge, a patient fails to understand how to administer the medication properly, misuse, or non-adherence (Cameron et al. 2010). Moreover, this pivotal dispute of misconception and ignorance of directions for prescribed pharmaceutical products, particularly for people with poor health literacy (Cameron et al. 2010), is associated with avoidance of preventive care and worse outcomes in many chronic medical conditions (Berkman et al. 2011). Thus, we hypothesized the following:
H3. Understanding of COVID-19 had a significant direct effect on perceived behavioral control of an individual.
Based on the Theory of Planned Behavior, the behavioral intent of a person is profoundly affected by the behavioral norms of its social community (Ajzen, 2011). However, in a technology context, protective technologies are significantly influenced by the user’s knowledge and the implications of technologies (Dinev & Hu, 2007). Moreover, Biglan and Taylor (2000) stated the process of educating the public of issues was found to influence the growing collective organizational network which actively supports policy strategies to reduce the complications. A critical stage in this approach is a thorough exposition of the problem that was accomplished by intensive engagement with the various communities, leading to stronger cultural norms (Biglan & Taylor, 2000). Thus, it is logical to conclude that the greater the group standards, the higher the level of understanding among the members of the social community. Hence, we proposed:
H4. Understanding of COVID-19 had a significant direct effect on subjective norms about the preventive measures implemented in the Philippines.
Some studies have observed that healthcare professionals exhibit a greater understanding, optimistic attitudes towards the pandemic, and frequently exhibit low rates of anxiety (Mishra et al. 2016). On the contrary, a research from Ethiopia recommended intensive preparation for health care practitioners due to their poor knowledge and erroneous beliefs about Ebola virus (Abebe et al. 2016). Meanwhile, during the H1N1 pandemic in 2016, a study conducted in Trinidad and Tobago reported that a substantial proportion of the general population is still oblivious of the severity and the prevention measures of the epidemic (Johnson & Hariharan, 2017). A related study was found that most respondents among secondary children in Nigeria had insufficient awareness and had a negative approach to Ebola virus (Ilesami et al. 2014). Along with hypotheses 1 and 2, we also hypothesized the following:
H5. Understanding of Covid-19 had a significant direct effect on an individual’s attitude towards the ongoing global outbreak.
Most studies about health-related behaviors have utilized TPB (Gabriel et al., 2019, Hagger et al., 2016, Huang et al., 2020) or PMT separately (Barati et al., 2020). It specifies that attitudes, subjective norms, and perceived behavioral control are the essential components of behavioral intent and actual health-related behavior (Kam et al. 2009). However, there is little to none have discussed a relationship between TPB and PMT. Hence, the current model attempts to correlate factors associated with PMT to the key determinants of TPB. Thus, we hypothesized the following:
H6. Perceived vulnerability had a significant direct effect on perceived behavioral control.
H7. Perceived severity had a significant direct effect on perceived behavioral control.
H8. Perceived vulnerability had a significant direct effect on subjective norms.
H9. Perceived severity had a significant direct effect on subjective norms.
H10. Perceived vulnerability had a significant direct effect on an individual’s attitude towards COVID-19.
H11. Perceived severity had a significant direct effect on an individual’s attitude towards COVID-19.
Lau et al. (2010) mentioned that the variables of behavioral intention is directly correlated to the factors derived from the TPB. According to Ajzen (1991), self-efficacy is an element that is common to both TPB and PMT which has the same concept of perceived behavioral control. It highlights the skills and competencies of a person handling the task or decision making (Bandura, 1997; Bandura, 1978). In fact, some studies (Workman et al. 2008) shows that self-efficacy has a significant effect on the capability of a person to perform task behavior. Hence, we hypothesize the following:
H12. Perceived behavioral control had a significant direct effect on intention to follow the preventive measures of COVID-19.
Subjective norm is defined as the normative rewards, values, and desires to adhere to a specific act which is primarily told by observation of others’ behaviors (Ajzen, 1991; Aronson et al. 2010). Past studies (Chan et al. 2005) have reported that the behavior of an individual is affected by the norm in their community. In the context of the individual’s compliance in organizations, Chan et al. (2005) stated that when supervisors and employees collogue cooperate and follow the guidelines, employees are most likely to comply with the organization’s guidelines. In addition, most studies have proven that there is a strong relationship between subjective norms and compliance in organizations (Ho et al., 2017, Schepers and Wetzels, 2007, Grimes and Marquardson, 2019). However, some studies mentioned that subjective norms construct are usually a poor indicator of behavioral intention since it is possible that the behavior of a person are affected by societal influences and personal choices (Armitage & Conner, 2001). Thus, we hypothesized the following:
H13. Subjective norms had a significant direct effect on an individual’s behavioral intention to follow the preventive measures of COVID-19.
Attitude towards behavior refers to the perception of the individual performing a particular behavior. Most studies have confirmed that people will comply with rules, requirements, and guidelines when they have a positive attitude (Mcmillan and Conner, 2003, Sasse et al., 2004, Ng et al., 2009, Herath and Rao, 2009, Bulgurcu et al., 2010). On the other hand, people who disregard certain desirable habits do not adhere readily to the implemented guidelines (Pahnila et al., 2007, Myyry et al., 2009). Hence, we hypothesized that:
H14. Attitude had a significant direct effect on an individual’s behavioral intention to follow the preventive measures of COVID-19.
Behavioral intent is defined as the subjective probability that an individual will execute a particular behavior (Mahardika et al. 2020). It is depicted as the best possible predictor of an individual’s action despite the many factors that may affect the effectivity of the intention-behavior relationship (Bentler & Speckart, 1979). Furthermore, when an actual behavior is deliberated upon an individual’s behavioral intention judgment, the behavioral intention will be foresighted. Hence Bentler & Speckart et al., (1979) proposed a model that complements Hill et al. (1977) theory in predicting behavior. Thus, we hypothesized the following:
H15. Behavioral intention had a significant direct effect on the actual behavior of individuals.
H16. Behavioral intention had a significant direct effect on the adapted behavior of individuals.
Previous studies emphasized that the feeling that individuals can only have a negligible impact on a larger scale as an essential barrier for socially responsible behavior (Ellen et al. 1991). According to Owens (2000), individuals may perceive their actions as irrelevant on a larger scale even if they have awareness regarding the situation and have a desire to have a contribution in society. Consequently, in addition to maintaining prosocial values, the perception that one’s actions will make a difference is a necessary precursor to socially responsible behavior. For example, in a study of social activism, only those individuals who perceived their actions as effective acted on their beliefs (Hinkle et al., 1996). Hence, we hypothesized the following:
H17. Actual behavior had a significant direct effect on the perceived effectiveness of COVID-19 preventive measures implemented in the Luzon, Philippines.
H18. Adapted behavior had a significant direct effect on the perceived effectiveness of COVID-19 preventive measures implemented in the Luzon, Philippines.
The current study integrated Protection Motivation Theory (PMT) and extended Theory of Planned Behavior (TPB) to evaluate factors affecting perceived effectiveness of COVID-19 prevention measures among Filipino during Enhanced Community Quarantine (ECQ) in Luzon, Philippines.
SEM was utilized to analyze the interrelationship among understanding of COVID-19 (U), perceived vulnerability (PV), perceived severity (PS), attitude (AT), subjective norm (SBN), perceived behavioral control (PBC), intention to follow (IF), actual behavior (AB), adapted behavior (AD), and perceived effectiveness (PE). An online questionnaire was utilized and a total of 649 data were collected.
SEM indicated that understanding of COVID-19 had significant direct effects on PV (β:0.247, p = 0.001) and PS (β:0.739, p = 0.001). Khosravi (2020) stated that the public’s assessment of the danger of the disease is influenced by their understanding of a specific health hazard by perceived severity and vulnerability.
Understanding of COVID-19 which related to the transmission, incubation period of the virus, the protocol when they catch symptoms that may lead to COVID-19, and the hospitals which can treat COVID-19 patients would positively affect the perceived vulnerability and perceived severity.
However, Reuben et al. (2020) mentioned in their study that people under-privileged and vulnerable people especially the older adults, unemployed, illiterates, farmers, rural and semi-urban residents are more likely to have poor knowledge about the COVID-19 due to the limited or no access to gadgets and internet.
Hence, policymakers have to make sure that the community fully understands about the virus and its symptoms as it can also enhance the perceived vulnerability and perceived severity of the disease.
As discussed before, the current study integrated the extended TPB wherein it was classified into three elements such as perceived behavioral control (PB), subjective norm (SN), and attitude (AT). Based on the result, it proved that understanding of COVID-19 had significant direct effects on SN (β:0.366; p = 0.001) and PBC (β:0.700; p = 0.001).
It could be interpreted that people can understand the virus if they are surrounded by people who are following the preventive protocols given by the government such as wearing face masks outside, staying and working from home, using hand sanitizer frequently, and practicing social distancing during the outbreak.
Moreover, people are more confident if they understand the symptoms of COVID-19.
Apart from the significant direct effects, the current model surprisingly showed that understanding COVID-19 had no significant direct effect on attitude but it was found to have a significant indirect effect.
Regarding the perceived vulnerability, SEM indicated that PV had significant negative effects on PBC (β:-0.136; p = 0.009) and SN (β:-0.111; p = 0.033).
It could be considered that individuals who are confident with their knowledge about COVID-19 and surrounded by a community that aware of the healthy lifestyle are most likely to think they are not vulnerable to the disease.
Meanwhile, PV had a significant effect on AT (β:0.092, p = 0.018) which supports the claim of Prokop and Kubiatko (2013) that perceived vulnerability to disease is positively correlated with attitude.
Hence, people who are worried about the number of people infected by COVID-19 are most likely to think they are vulnerable to the disease.
On the other hand, PS was found to have positive effects on PBC (β: 0.231, p = 0.028), SN (β:0.260; p = 0.003), and AT (β:0.876; p = 0.001).
The result indicates that people who perceived the severity of COVID-19 are more likely to have enough knowledge about the virus. Moreover, an individual who is surrounded by a community that aware with a healthy lifestyle will help them to understand how severe COVID-19 is.
Regarding the intention to follow, the current model proved Lau et al. (2010) statement that factors derived from the TPB were significantly associated with behavioral intention. The result showed that intention to follow were significantly affected by PBC (β:0.454; p = 0.001), SBN (β:0.168; p = 0.001), and AT (β:0.413; p = 0.001) which makes several implications.
First, based on PBC, such as people with enough knowledge is more likely to stay at home and comply with the lockdown implementation of the country, city, and community. In a similar study of Seale’s et al. (2020), it was observed that strategies implemented by the government to subsidize the outbreak such as social distancing and lowering the score if people do not fully understand the government strategy could influence on the wellbeing of the community.
Second, based on SBN, individuals who are surrounded by people that are frequently using hand sanitizer is more likely to follow the recommended precautions during the COVID-19 outbreak. Lastly, based on AT, people who are worried about the number of infected people are most likely to follow every rule implemented by the government during the outbreak.
Meanwhile, it was found that the intention to follow had significant effects on AB (β:0.794; p = 0.001) and AD (β:0.928; p = 0.001). The result implies that the people’s willingness to follow leads to a healthy behavior towards the recommended precautions.
In addition, intention to follow tends to affect a few implications such as proper hygiene and a healthy lifestyle. The people are more likely to wash their hands, wear face masks, and avoid activities would affect their immunity during the COVID-19 outbreak.
Subsequently, the result showed that AB was found to have a significant effect on PE (β: 0.163; p = 0.001). These would make an implication in which proper hand washing, use of microbial solutions, and social distancing would enhance the PE.
The policymakers should implement these to create awareness and to prevent panic within the community (Balkhy et al. 2010). On the other hand, the SEM showed that AD would have a significant effect on PE (β:0.679; p = 0.001). The SEM indicated that a healthy lifestyle of people would help to achieve PE by increasing their immunity to avoid the transmission of the disease.
Finally, the result implies that a healthy lifestyle, social distancing, face mask, proper hygiene, and lockdown would enhance the perceived effectiveness of COVID-19 preventive measures.
Surprisingly, the Understanding of COVID-19 had a significant indirect effect on perceived effectiveness (β:0.567; p = 0.001). Educating the community particularly related to the transmission, the incubation period, the symptoms, the protocol if they have the symptoms, and the hospital that can treat COVID-19 would significantly lead to the perceived effectiveness of the prevention measures. Our results proved that the government and other stakeholders really need to educate the community about the COVID-19 including the prevention measures.
In the Philippines, there are different quarantine levels that Inter-Agency Task Force for the Management of Emerging Infectious Diseases (IATF-EID) recommended which are Enhanced Community Quarantine (ECQ), Modified Enhanced Community Quarantine (MECQ) for high-risk areas and General Community Quarantine (GCQ) for moderate risk areas (Ranada, 2020).
Cities such as Metro Manila, Laguna, and Cebu City were placed under MECQ starting from May 16 until May 31, 2020. MECQ, as defined by the IATF-EID, is the transition phase between ECQ and GCQ. During this phase, residents are still required to follow the implemented protocol by the government especially staying at home for people who are considered vulnerable and transmitters.
Currently, some regions (e.g. Region II, Region III, Region IV, NCR) and provinces (e.g. Albay and Pangasinan) in Luzon are placed under GCQ in which it has more relaxed measures compared to MECQ (GOVPH, 2020). The difference of GCQ and MECQ is that during the GCQ, transportation excluding the jeepneys and buses is now allowed but not in full capacity in compliance with strict social distancing (Esguerra, 2020).
All industries and businesses are allowed to operate up to 75% and must follow strict protocols. Although our study was conducted during ECQ, our results are still valid during MECQ and GCQ since the implemented preventive measures of the government are still in effect.
Despite the clear and substantial contributions, the authors would like to acknowledge several limitations of this study. First, the current study was mainly focused on the perceived effectiveness rather than measuring the effectiveness of prevention measures itself. A future study to correlate the perceived effectiveness and the number of cases would be a very promising topic.
Second, our sample was collected through an online questionnaire. Future research to collect more samples in the community after ECQ would lead to more comprehensive results. Last but not the least, we did not correlate the power of media to the understanding of COVID-19. Future studies should also incorporate the trust of people in information and outcome expectations as one of the essential predictors of behavioral intention to follow.
COVID-19 pandemic is a global crisis in 2020. It has grown extensively to infect 10,185,374 people worldwide and lead to 503,862 death cases as of June 30th, 2020 (WHO, 2020).
In the Philippines, there had been 36,438 confirmed cases of the infection and 1,255 death cases as of June 29th 2020, leading the government to do several preventive measures such as ECQ in Luzon. The current study integrated Protection Motivation Theory (PMT) and extended Theory of Planned Behavior (TPB) to evaluate factors affecting the perceived effectiveness of COVID-19 prevention measures among Filipino during Enhanced Community Quarantine (ECQ) in Luzon, Philippines.
A total of 649 Filipino answered the online questionnaire which contained of 63 questions. The results of Structural Equation Modeling (SEM) indicated that understanding of COVID-19 had significant direct effects on perceived vulnerability and perceived severity. In addition, perceived vulnerability and perceived severity had significant indirect effects on intention to follow.
Intention to follow had significant direct effects on actual behavior and adapted behavior which subsequently lead to perceived effectiveness.
Interestingly, understanding of COVID-19 was found to have a significant indirect effect on perceived effectiveness.
The current study is one of the first studies that analyzed factors affecting perceived effectiveness of COVID-19 prevention measures during the global pandemic. Finally, the integrated PMT and extended TPB of this study can be applied and extended to evaluate the perceived effectiveness of COVID-19 measures in other countries that currently dealing with COVID-19 pandemic.
More information: Stefan Thurner et al, A network-based explanation of why most COVID-19 infection curves are linear, Proceedings of the National Academy of Sciences (2020). DOI: 10.1073/pnas.2010398117